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.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Severe aortic stenosis detection by deep learning applied to echocardiography.

BACKGROUND AND AIMS: Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS: In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION: This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.

Machine learning in precision diabetes care and cardiovascular risk prediction.

Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.

A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST).

AIMS: Coronary artery disease is frequently diagnosed following evaluation of stable chest pain with anatomical or functional testing. A more granular understanding of patient phenotypes that benefit from either strategy may enable personalized testing. METHODS AND RESULTS: Using participant-level data from 9572 patients undergoing anatomical (n = 4734) vs. functional (n = 4838) testing in the PROMISE (PROspective Multicenter Imaging Study for Evaluation of Chest Pain) trial, we created a topological representation of the study population based on 57 pre-randomization variables. Within each patient's 5% topological neighbourhood, Cox regression models provided individual patient-centred hazard ratios for major adverse cardiovascular events and revealed marked heterogeneity across the phenomap [median 1.11 (10th to 90th percentile: 0.52-2.61]), suggestive of distinct phenotypic neighbourhoods favouring anatomical or functional testing. Based on this risk phenomap, we employed an extreme gradient boosting algorithm in 80% of the PROMISE population to predict the personalized benefit of anatomical vs. functional testing using 12 model-derived, routinely collected variables and created a decision support tool named ASSIST (Anatomical vs. Stress teSting decIsion Support Tool). In both the remaining 20% of PROMISE and an external validation set consisting of patients from SCOT-HEART (Scottish COmputed Tomography of the HEART Trial) undergoing anatomical-first vs. functional-first assessment, the testing strategy recommended by ASSIST was associated with a significantly lower incidence of each study's primary endpoint (P = 0.0024 and P = 0.0321 for interaction, respectively), as well as a harmonized endpoint of all-cause mortality or non-fatal myocardial infarction (P = 0.0309 and P < 0.0001 for interaction, respectively). CONCLUSION: We propose a novel phenomapping-derived decision support tool to standardize the selection of anatomical vs. functional testing in the evaluation of stable chest pain, validated in two large and geographically diverse clinical trial populations.

Effects of canagliflozin on human myocardial redox signalling: clinical implications.

AIMS: Recent clinical trials indicate that sodium-glucose cotransporter 2 (SGLT2) inhibitors improve cardiovascular outcomes in heart failure patients, but the underlying mechanisms remain unknown. We explored the direct effects of canagliflozin, an SGLT2 inhibitor with mild SGLT1 inhibitory effects, on myocardial redox signalling in humans. METHODS AND RESULTS: Study 1 included 364 patients undergoing cardiac surgery. Right atrial appendage biopsies were harvested to quantify superoxide (O2.-) sources and the expression of inflammation, fibrosis, and myocardial stretch genes. In Study 2, atrial tissue from 51 patients was used ex vivo to study the direct effects of canagliflozin on NADPH oxidase activity and nitric oxide synthase (NOS) uncoupling. Differentiated H9C2 and primary human cardiomyocytes (hCM) were used to further characterize the underlying mechanisms (Study 3). SGLT1 was abundantly expressed in human atrial tissue and hCM, contrary to SGLT2. Myocardial SGLT1 expression was positively associated with O2.- production and pro-fibrotic, pro-inflammatory, and wall stretch gene expression. Canagliflozin reduced NADPH oxidase activity via AMP kinase (AMPK)/Rac1signalling and improved NOS coupling via increased tetrahydrobiopterin bioavailability ex vivo and in vitro. These were attenuated by knocking down SGLT1 in hCM. Canagliflozin had striking ex vivo transcriptomic effects on myocardial redox signalling, suppressing apoptotic and inflammatory pathways in hCM. CONCLUSIONS: We demonstrate for the first time that canagliflozin suppresses myocardial NADPH oxidase activity and improves NOS coupling via SGLT1/AMPK/Rac1 signalling, leading to global anti-inflammatory and anti-apoptotic effects in the human myocardium. These findings reveal a novel mechanism contributing to the beneficial cardiac effects of canagliflozin.

Cardiac Computed Tomography: Assessment of Coronary Inflammation and Other Plaque Features.

Unstable coronary plaques that are prone to erosion and rupture are the major cause of acute coronary syndromes. Our expanding understanding of the biological mechanisms of coronary atherosclerosis and rapid technological advances in the field of medical imaging has established cardiac computed tomography as a first-line diagnostic test in the assessment of suspected coronary artery disease, and as a powerful method of detecting the vulnerable plaque and patient. Cardiac computed tomography can provide a noninvasive, yet comprehensive, qualitative and quantitative assessment of coronary plaque burden, detect distinct high-risk morphological plaque features, assess the hemodynamic significance of coronary lesions and quantify the coronary inflammatory burden by tracking the effects of arterial inflammation on the composition of the adjacent perivascular fat. Furthermore, advances in machine learning, computational fluid dynamic modeling, and the development of targeted contrast agents continue to expand the capabilities of cardiac computed tomography imaging. In our Review, we discuss the current role of cardiac computed tomography in the assessment of coronary atherosclerosis, highlighting its dual function as a clinical and research tool that provides a wealth of structural and functional information, with far-reaching diagnostic and prognostic implications.

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography.

BACKGROUND: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. METHODS AND RESULTS: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI. CONCLUSION: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.

Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data.

BACKGROUND: Coronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker-the perivascular fat attenuation index (FAI)-captures coronary inflammation by mapping spatial changes of perivascular fat attenuation on coronary computed tomography angiography (CTA). However, the ability of the perivascular FAI to predict clinical outcomes is unknown. METHODS: In the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study, we did a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary CTA in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries-the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. We assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features. FINDINGS: Between 2005 and 2009, 1872 participants in the derivation cohort underwent coronary CTA (median age 62 years [range 17-89]). Between 2008 and 2016, 2040 patients in the validation cohort had coronary CTA (median age 53 years [range 19-87]). Median follow-up was 72 months (range 51-109) in the derivation cohort and 54 months (range 4-105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR] 2·15, 95% CI 1·33-3·48; p=0·0017 in the derivation cohort, and 2·06, 1·50-2·83; p<0·0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as -70·1 Hounsfield units (HU) or higher in the derivation cohort (HR 9·04, 95% CI 3·35-24·40; p<0·0001 for cardiac mortality; 2·55, 1·65-3·92; p<0·0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR 5·62, 95% CI 2·90-10·88; p<0·0001 for cardiac mortality; 3·69, 2·26-6·02; p<0·0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality. INTERPRETATION: The perivascular FAI enhances cardiac risk prediction and restratification over and above current state-of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation. High perivascular FAI values (cutoff ≥-70·1 HU) are an indicator of increased cardiac mortality and, therefore, could guide early targeted primary prevention and intensive secondary prevention in patients. FUNDING: British Heart Foundation, and the National Institute of Health Research Oxford Biomedical Research Centre.

Myocarditis Surveillance in Patients with Advanced Melanoma on Combination Immune Checkpoint Inhibitor Therapy: The Memorial Sloan Kettering Cancer Center Experience.

This letter to the editor describes myocarditis screening among patients undergoing combination immune checkpoint inhibitor therapy, in light of the consensus document from the Checkpoint Inhibitor Safety Working Group.

Stem Cell Therapy for Congenital Heart Disease: A Systematic Review.

BACKGROUND: Congenital heart disease (CHD) constitutes the most prevalent and heterogeneous group of congenital anomalies. Although surgery remains the gold standard treatment modality, stem cell therapy has been gaining ground as a complimentary or alternative treatment option in certain types of CHD. The aim of this study was to present the existing published evidence and ongoing research efforts on the implementation of stem cell-based therapeutic strategies in CHD. METHODS: A systematic review was conducted by searching Medline, ClinicalTrials.gov, and the Cochrane library, along with reference lists of the included studies through April 23, 2017. RESULTS: Nineteen studies were included in this review (8 preclinical, 6 clinical, and 5 ongoing trials). Various routes of cardiac stem cell delivery have been reported, including intracoronary, intramyocardial, intravenous, and epicardial. Depending on their origin and level of differentiation at which they are harvested, stem cells may exhibit different properties. Preclinical studies have mostly focused on modeling right ventricle dysfunction or failure and pulmonary artery hypertension by using pressure or volume overload in vitro or in vivo. Only a limited number of clinical trials on patients with CHD exist, and these primarily focus on hypoplastic left heart syndrome. Cell-based tissue engineering has recently been introduced, and research currently is focusing on developing cell-seeded grafts and patches that could potentially grow in parallel with whole body growth once implanted in the heart. CONCLUSIONS: It seems that stem cell delivery to the diseased heart as an adjunct to surgical palliation may provide some benefits over surgery alone in terms of cardiac function, somatic growth, and quality of life. Despite encouraging preliminary results, stem cell therapies for patients with CHD should only be considered in the setting of well-designed clinical trials. More wet laboratory research experience is needed, and translation of promising findings to large clinical studies is warranted to clearly define the efficacy and safety profile of this alternative and potentially groundbreaking therapeutic approach.

Development and Validation of a Clinical Score for Cardiovascular Risk Stratification of Long-Term Childhood Cancer Survivors.

BACKGROUND: Long-term childhood cancer survivors (CCS) are at increased risk of adverse cardiovascular events; however, there is a paucity of risk-stratification tools to identify those at higher-than-normal risk. SUBJECTS, MATERIALS, AND METHODS: This was a population-based study using data from the Surveillance, Epidemiology, and End Results Program (1973-2013). Long-term CCS (age at diagnosis ≤19 years, survival ≥5 years) were followed up over a median time period of 12.3 (5-40.9) years. Independent predictors of cardiovascular mortality (CVM) were combined into a risk score, which was developed in a derivation set (n = 22,374), and validated in separate patient registries (n = 6,437). RESULTS: In the derivation registries, older age at diagnosis (≥10 years vs. reference group of 1-5 years), male sex, non-white race, a history of lymphoma, and a history of radiation were independently associated with an increased risk of CVM among long-term CCS (p < .05). A risk score derived from this model (Childhood and Adolescence Cancer Survivor CardioVascular score [CHACS-CV], range: 0-8) showed good discrimination for CVM (Harrell's C-index [95% confidence interval (CI)]: 0.73 [0.68-0.78], p < .001) and identified a high-risk group (CHACS-CV ≥6), with cumulative CVM incidence over 30 years of 6.0% (95% CI: 4.3%-8.1%) versus 2.6% (95% CI: 1.8%-3.7%), and 0.7% (95% CI: 0.5%-1.0%) in the mid- (CHACS-CV = 4-5) and low-risk groups (CHACS-CV ≤3), respectively (plog-rank < .001). In the validation set, the respective cumulative incidence rates were 4.7%, 3.1%, and 0.8% (plog-rank < .001). CONCLUSION: We propose a simple risk score that can be applied in everyday clinical practice to identify long-term CCS at increased cardiovascular risk, who may benefit from early cardiovascular screening, and risk-reduction strategies. IMPLICATIONS FOR PRACTICE: Childhood cancer survivors (CCS) are known to be at increased cardiovascular risk. Currently available prognostic tools focus on treatment-related adverse events and late development of congestive heart failure, but there is no prognostic model to date to estimate the risk of cardiovascular mortality among long-term CCS. A simple clinical tool is proposed for cardiovascular risk stratification of long-term CCS based on easily obtainable information from their medical history. This scoring system may be used as a first-line screening tool to assist health care providers in identifying those who may benefit from closer follow-up and enable timely deployment of preventive strategies.

Pregnancy associated plasma protein-A as a prognostic biomarker of all-cause mortality and cardiovascular events in patients presenting with chest pain: a systematic review.

AIM: Novel biomarkers have been proposed for identification of patients at greater risk of future adverse events among those presenting with chest pain. In this review, we aim to elucidate the ability of pregnancy associated plasma protein-A (PAPP-A) to predict mortality and other cardiovascular events in this patient population. METHODS: A literature search of the electronic databases Medline, Scopus, Cochrane Library and ClinicalTrials.gov was performed in order to identify studies investigating the utility of PAPP-A to predict mortality and adverse cardiovascular events in patients with chest pain. RESULTS: Eight studies met our inclusion criteria. Five of these studies pertained to patients with confirmed ischemic chest pain, while the rest included patients presenting with chest pain possibly due to acute coronary syndrome, irrespectively of the underlying cause. Although the results for long-term events were inconclusive in both groups of patients, higher PAPP-A concentrations were found to be a significant predictor of short-term adverse events in patients with confirmed ischemic chest pain. CONCLUSIONS: PAPP-A appears to be a potentially useful biomarker for short-term risk stratification of patients presenting with chest pain of ischemic origin. However, there is an eminent need for more standardized clinical studies investigating the prognostic value of this biomarker.

Predictive value of telomere length on outcome following acute myocardial infarction: evidence for contrasting effects of vascular vs. blood oxidative stress.

AIMS: Experimental evidence suggests that telomere length (TL) is shortened by oxidative DNA damage, reflecting biological aging. We explore the value of blood (BTL) and vascular TL (VTL) as biomarkers of systemic/vascular oxidative stress in humans and test the clinical predictive value of BTL in acute myocardial infarction (AMI). METHODS AND RESULTS: In a prospective cohort of 290 patients surviving recent AMI, BTL measured on admission was a strong predictor of all-cause [hazard ratio (HR) [95% confidence interval (CI)]: 3.21 [1.46-7.06], P = 0.004] and cardiovascular mortality (HR [95% CI]: 3.96 [1.65-9.53], P = 0.002) 1 year after AMI (for comparisons of short vs. long BTL, as defined by a T/S ratio cut-off of 0.916, calculated using receiver operating characteristic analysis; P adjusted for age and other predictors). To explore the biological meaning of these findings, BTL was quantified in 727 consecutive patients undergoing coronary artery bypass grafting (CABG), and superoxide (O2.-) was measured in peripheral blood mononuclear cells (PBMNC). VTL/vascular O2.- were quantified in saphenous vein (SV) and mammary artery (IMA) segments. Patients were genotyped for functional genetic polymorphisms in P22ph°x (activating NADPH-oxidases) and vascular smooth muscle cells (VSMC) selected by genotype were cultured from vascular tissue. Short BTL was associated with high O2.- in PBMNC (P = 0.04) but not in vessels, whereas VTL was related to O2.- in IMA (ρ = -0.49, P = 0.004) and SV (ρ = -0.52, P = 0.01). Angiotensin II (AngII) incubation of VSMC (30 days), as a means of stimulating NADPH-oxidases, increased O2.- and reduced TL in carriers of the high-responsiveness P22ph°x alleles (P = 0.007). CONCLUSION: BTL predicts cardiovascular outcomes post-AMI, independently of age, whereas VTL is a tissue-specific (rather than a global) biomarker of vascular oxidative stress. The lack of a strong association between BTL and VTL reveals the importance of systemic vs. vascular factors in determining clinical outcomes after AMI.

Prognostic implications of epicardial fat volume quantification in acute pericarditis.

BACKGROUND: The pathophysiology of acute pericarditis remains largely unknown, and biomarkers are needed to identify patients susceptible to complications. As adipose tissue has a pivotal role in cardiovascular disease pathogenesis, we hypothesized that quantification of epicardial fat volume (EFV) provides prognostic information in patients with acute pericarditis. MATERIALS AND METHODS: Fifty (n = 50) patients with first diagnosis of acute pericarditis were enrolled in this study. Patients underwent a cardiac computerized tomography (CT) scan to quantify EFV on a dedicated workstation. Patients were followed up in hospital for atrial fibrillation (AF) development and up to 18 months for the composite clinical endpoint of development of constrictive, recurrent or incessant pericarditis or poor response to nonsteroidal anti-inflammatory drugs. RESULTS: Patients presenting with chest pain had lower EFV vs. patients without chest pain (167·2 ± 21·7 vs. 105·1 ± 11·1 cm3 , respectively, P < 0·01); EFV (but not body mass index) was strongly positively correlated with pericardial effusion size (r = 0·395, P = 0·007) and associated with in-hospital AF. At follow-up, patients that reached the composite clinical endpoint had lower EFV (P < 0·05). After adjustment for age, EFV was associated with lower odds ratio for the composite clinical endpoint point of poor response to NSAIDs or the development of constrictive, recurrent or incessant pericarditis during follow-up (per 20 cm3 increase in EFV: OR = 0·802 [0·656-0·981], P < 0·05). CONCLUSIONS: We report for the first time a significant association of EFV with the clinical features and the outcome of patients with acute pericarditis. Measurement of EFV by CT may have important prognostic implications in these patients.

Role of deferred stenting in patients with ST elevation myocardial infarction treated with primary percutaneous coronary intervention: A systematic review and meta-analysis.

OBJECTIVES: We conducted a meta-analysis of studies comparing deferred stenting strategy versus the conventional approach with immediate stenting in patients with ST elevation myocardial infarction. BACKGROUND: Deferring stent after mechanical flow restoration has been proposed as a strategy to reduce the risk of "no reflow" in patients with STEMI undergoing primary percutaneous coronary intervention (pPCI). Conflicting evidence is available currently, especially after the recent publication of three randomized clinical trials. METHODS: Searches in electronic databases were performed. Comparisons between the two strategies were performed for both hard clinical endpoints (all cause-mortality, cardiovascular mortality, unplanned revascularization, myocardial infarction and readmission for heart failure) and surrogate angiographic endpoints (TIMI flow < 3 and myocardial blush grade (MBG) < 2). RESULTS: Eight studies (three randomized and five non-randomized) were deemed eligible, accounting for a total of 2101 patients. No difference in terms of hard clinical endpoints was observed between deferred and immediate stenting (OR [95% CI]: 0.79 [0.54-1.15], for all-cause mortality; odds ratio (OR) [95% CI]: 0.79 [0.47-1.31] for cardiovascular mortality; OR [95% CI]: 0.95 [0.64-1.41] for myocardial infarction; OR [95% CI]: 1.37 [0.87-2.16], for unplanned revascularization and OR [95% CI]: 0.50 [0.21-1.17] for readmission for heart failure). Notably, the deferred stenting approach was associated with improved outcome of the surrogate angiographic endpoints (OR [95% CI]: 0.43 [0.18-0.99] of TIMI flow < 3 and OR [95% CI]: 0.25 [0.11-0.57] for MBG < 2. CONCLUSIONS: A deferred stenting strategy could be a feasible alternative to the conventional approach with immediate stenting in "selected" STEMI patients undergoing pPCI.

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remains unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance and the importance of vendor-agnostic data types that will be immediately available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.