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.

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.

Prognostic Significance of Different Ventricular Ectopic Burdens During Submaximal Exercise in Asymptomatic UK Biobank Subjects.

BACKGROUND: The consequences of exercise-induced premature ventricular contractions (PVCs) in asymptomatic individuals remain unclear. This study aimed to assess the association between PVC burdens during submaximal exercise and major adverse cardiovascular events (MI/HF/LTVA: myocardial infarction [MI], heart failure [HF], and life-threatening ventricular arrhythmia [LTVA]), and all-cause mortality. Additional end points were MI, LTVA, HF, and cardiovascular mortality. METHODS: A neural network was developed to count PVCs from ECGs recorded during exercise (6 minutes) and recovery (1 minute) in 48 315 asymptomatic participants from UK Biobank. Associations were estimated using multivariable Cox proportional hazard models. Explorative studies were conducted in subgroups with cardiovascular magnetic resonance imaging data (n=6290) and NT-proBNP (N-terminal Pro-B-type natriuretic peptide) levels (n=4607) to examine whether PVC burden was associated with subclinical cardiomyopathy. RESULTS: Mean age was 56.8±8.2 years; 51.1% of the participants were female; and median follow-up was 12.6 years. Low PVC counts during exercise and recovery were both associated with MI/HF/LTVA risk, independently of clinical factors: adjusted hazard ratio (HR), 1.2 (1-5 exercise PVCs, P<0.001) and HR, 1.3 (1-5 recovery PVCs, P<0.001). Risks were higher with increasing PVC count: HR, 1.8 (>20 exercise PVCs, P<0.001) and HR, 1.6 (>5 recovery PVCs, P<0.001). A similar trend was observed for all-cause mortality, although associations were only significant for high PVC burdens: HRs, 1.6 (>20 exercise PVCs, P<0.001) and 1.5 (>5 recovery PVCs, P<0.001). Complex PVC rhythms were associated with higher risk compared with PVC count alone. PVCs were also associated with incident HF, LTVA, and cardiovascular mortality, but not MI. In the explorative studies, high PVC burden was associated with larger left ventricular volumes, lower ejection fraction, and higher levels of NT-proBNP compared with participants without PVCs. CONCLUSIONS: In this cohort of middle-aged and older adults, PVC count during submaximal exercise and recovery were both associated with MI/HF/LTVA, all-cause mortality, HF, LTVAs, and cardiovascular mortality, independent of clinical and exercise test factors, indicating an incremental increase in risk as PVC count rises. Complex PVC rhythms were associated with higher risk compared with PVC count alone. Underlying mechanisms may include the presence of subclinical cardiomyopathy.

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.

NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank.

BACKGROUND: The NHS Health Check is a preventive programme in the UK designed to screen for cardiovascular risk and to aid in primary disease prevention. Despite its widespread implementation, the effectiveness of the NHS Health Check for longer-term disease prevention is unclear. In this study, we measured the rate of new diagnoses in UK Biobank participants who underwent the NHS Health Check compared with those who did not. METHODS: Within the UK Biobank prospective study, 48,602 NHS Health Check recipients were identified from linked primary care records. These participants were then covariate-matched on an extensive range of socio-demographic, lifestyle, and medical factors with 48,602 participants without record of the check. Follow-up diagnoses were ascertained from health records over an average of 9 years (SD 2 years) including hypertension, diabetes, hypercholesterolaemia, stroke, dementia, myocardial infarction, atrial fibrillation, heart failure, fatty liver disease, alcoholic liver disease, liver cirrhosis, liver failure, acute kidney injury, chronic kidney disease (stage 3 +), cardiovascular mortality, and all-cause mortality. Time-varying survival modelling was used to compare adjusted outcome rates between the groups. RESULTS: In the immediate 2 years after the NHS Health Check, higher diagnosis rates were observed for hypertension, high cholesterol, and chronic kidney disease among health check recipients compared to their matched counterparts. However, in the longer term, NHS Health Check recipients had significantly lower risk across all multiorgan disease outcomes and reduced rates of cardiovascular and all-cause mortality. CONCLUSIONS: The NHS Health Check is linked to reduced incidence of disease across multiple organ systems, which may be attributed to risk modification through earlier detection and treatment of key risk factors such as hypertension and high cholesterol. This work adds important evidence to the growing body of research supporting the effectiveness of preventative interventions in reducing longer-term multimorbidity.

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

OBJECTIVES: To summarize the status of the SCMR Registry at 150,000 exams. BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The 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 (DICOM) images. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and present 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). The human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% female, 72% Caucasian, and had a mortality rate of 8%. The most common indication was cardiomyopathy (27%), and most frequently used current procedural terminology (CPT) code was 75561 (35%). Macrocyclic gadolinium-based contrast agents represented 89% of contrast utilization after 2015. Short-axis cines were performed in 99% of scans, short-axis LGE in 66%, and stress perfusion sequences in 30%. Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction (LVEF) < 35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct late gadolinium enhancement (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. CONCLUSIONS: 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. CONDENSED ABSTRACT: The SCMR Registry is a central regulatory-compliant cloud-based repository for real-world clinical data and DICOM images for multicenter cardiovascular research, including outcomes-based data. The Registry contains 299,622,066 DICOM images and 456,678 patient-years follow-up. Data compiled from 154,458 CMR scans across 20 US sites demonstrated cardiomyopathy as the most common indication and 89% macrocyclic gadolinium contrast utilization after 2015. There was an overall mortality rate of 8%, with higher rates in those with LVEF<35%, abnormal wall motion, ischemia presence, or infarct LGE. The Registry aims to promote evidence-based CMR utilization through a collaborative effort to positively impact cardiovascular outcomes.

Competency-based cardiac imaging for patient-centred care. A statement of the European Society of Cardiology (ESC). With the contribution of the European Association of Cardiovascular Imaging (EACVI), and the support of the Association of Cardiovascular Nursing & Allied Professions (ACNAP), the Association for Acute CardioVascular Care (ACVC), the European Association of Preventive Cardiology (EAPC), the European Association of Percutaneous Cardiovascular Interventions (EAPCI), the European Heart Rhythm Association (EHRA), and the Heart Failure Association (HFA) of the ESC.

Imaging plays an integral role in all aspects of managing heart disease and cardiac imaging is a core competency of cardiologists. The adequate delivery of cardiac imaging services requires expertise in both imaging methodology-with specific adaptations to imaging of the heart-as well as intricate knowledge of heart disease. The European Society of Cardiology (ESC) and the European Association of Cardiovascular Imaging have developed and implemented a successful education and certification programme for all cardiac imaging modalities. This programme equips cardiologists to provide high quality competency-based cardiac imaging services ensuring they are adequately trained and competent in the entire process of cardiac imaging, from the clinical indication via selecting the best imaging test to answer the clinical question, to image acquisition, analysis, interpretation, storage, repository, and results dissemination. This statement emphasizes the need for competency-based cardiac imaging delivery which is key to optimal, effective and efficient, patient care.

Use of cardiac imaging in chronic coronary syndromes: the EURECA Imaging registry.

BACKGROUND: The prospective, multicentre EURECA registry assessed the use of imaging and adoption of the European Society of Cardiology (ESC) Guidelines (GL) in patients with chronic coronary syndromes (CCS). METHODS: Between May 2019 and March 2020, 5156 patients were recruited in 73 centres from 24 ESC member countries. The adoption of GL recommendations was evaluated according to clinical presentation and pre-test probability (PTP) of obstructive coronary artery disease (CAD). RESULTS: The mean age of the population was 64 ± 11 years, 60% of patients were males, 42% had PTP >15%, 27% had previous CAD, and ejection fraction was <50% in 5%. Exercise ECG was performed in 32% of patients, stress imaging as the first choice in 40%, and computed tomography coronary angiography (CTCA) in 22%. Invasive coronary angiography (ICA) was the first or downstream test in 17% and 11%, respectively. Obstructive CAD was documented in 24% of patients, inducible ischaemia in 19%, and 13% of patients underwent revascularization. In 44% of patients, the overall diagnostic process did not adopt the GL. In these patients, referral to stress imaging (21% vs. 58%; P < 0.001) or CTCA (17% vs. 30%; P < 0.001) was less frequent, while exercise ECG (43% vs. 22%; P < 0.001) and ICA (48% vs. 15%; P < 0.001) were more frequently performed. The adoption of GL was associated with fewer ICA, higher proportion of diagnosis of obstructive CAD (60% vs. 39%, P < 0.001) and revascularization (54% vs. 37%, P < 0.001), higher quality of life, fewer additional testing, and longer times to late revascularization. CONCLUSIONS: In patients with CCS, current clinical practice does not adopt GL recommendations on the use of diagnostic tests in a significant proportion of patients. When the diagnostic approach adopts GL recommendations, invasive procedures are less frequently used and the diagnostic yield and therapeutic utility are superior.

Frequency, Penetrance, and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants.

BACKGROUND: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. METHODS: UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. RESULTS: Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. CONCLUSIONS: In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.

Genome-wide association study of cardiac troponin I in the general population.

Circulating cardiac troponin proteins are associated with structural heart disease and predict incident cardiovascular disease in the general population. However, the genetic contribution to cardiac troponin I (cTnI) concentrations and its causal effect on cardiovascular phenotypes are unclear. We combine data from two large population-based studies, the Trøndelag Health Study and the Generation Scotland Scottish Family Health Study, and perform a genome-wide association study of high-sensitivity cTnI concentrations with 48 115 individuals. We further use two-sample Mendelian randomization to investigate the causal effects of circulating cTnI on acute myocardial infarction (AMI) and heart failure (HF). We identified 12 genetic loci (8 novel) associated with cTnI concentrations. Associated protein-altering variants highlighted putative functional genes: CAND2, HABP2, ANO5, APOH, FHOD3, TNFAIP2, KLKB1 and LMAN1. Phenome-wide association tests in 1688 phecodes and 83 continuous traits in UK Biobank showed associations between a genetic risk score for cTnI and cardiac arrhythmias, metabolic and anthropometric measures. Using two-sample Mendelian randomization, we confirmed the non-causal role of cTnI in AMI (5948 cases, 355 246 controls). We found indications for a causal role of cTnI in HF (47 309 cases and 930 014 controls), but this was not supported by secondary analyses using left ventricular mass as outcome (18 257 individuals). Our findings clarify the biology underlying the heritable contribution to circulating cTnI and support cTnI as a non-causal biomarker for AMI in the general population. Using genetically informed methods for causal inference helps inform the role and value of measuring cTnI in the general population.

Liver disease is a significant risk factor for cardiovascular outcomes - A UK Biobank study.

BACKGROUND & AIMS: Chronic liver disease (CLD) is associated with increased cardiovascular disease (CVD) risk. We investigated whether early signs of liver disease (measured by iron-corrected T1-mapping [cT1]) were associated with an increased risk of major CVD events. METHODS: Liver disease activity (cT1) and fat (proton density fat fraction [PDFF]) were measured using LiverMultiScan® between January 2016 and February 2020 in the UK Biobank imaging sub-study. Using multivariable Cox regression, we explored associations between liver cT1 (MRI) and primary CVD (coronary artery disease, atrial fibrillation [AF], embolism/vascular events, heart failure [HF] and stroke), and CVD hospitalisation and all-cause mortality. Liver blood biomarkers, general metabolism biomarkers, and demographics were also included. Subgroup analysis was conducted in those without metabolic syndrome (defined as at least three of: a large waist, high triglycerides, low high-density lipoprotein cholesterol, increased systolic blood pressure, or elevated haemoglobin A1c). RESULTS: A total of 33,616 participants (mean age 65 years, mean BMI 26 kg/m2, mean haemoglobin A1c 35 mmol/mol) had complete MRI liver data with linked clinical outcomes (median time to major CVD event onset: 1.4 years [range: 0.002-5.1]; follow-up: 2.5 years [range: 1.1-5.2]). Liver disease activity (cT1), but not liver fat (PDFF), was associated with higher risk of any major CVD event (hazard ratio 1.14; 95% CI 1.03-1.26; p = 0.008), AF (1.30; 1.12-1.51; p <0.001); HF (1.30; 1.09-1.56; p= 0.004); CVD hospitalisation (1.27; 1.18-1.37; p <0.001) and all-cause mortality (1.19; 1.02-1.38; p = 0.026). FIB-4 index was associated with HF (1.06; 1.01-1.10; p = 0.007). Risk of CVD hospitalisation was independently associated with cT1 in individuals without metabolic syndrome (1.26; 1.13-1.4; p <0.001). CONCLUSION: Liver disease activity, by cT1, was independently associated with a higher risk of incident CVD and all-cause mortality, independent of pre-existing metabolic syndrome, liver fibrosis or fat. IMPACT AND IMPLICATIONS: Chronic liver disease (CLD) is associated with a twofold greater incidence of cardiovascular disease. Our work shows that early liver disease on iron-corrected T1 mapping was associated with a higher risk of major cardiovascular disease (14%), cardiovascular disease hospitalisation (27%) and all-cause mortality (19%). These findings highlight the prognostic relevance of a comprehensive evaluation of liver health in populations at risk of CVD and/or CLD, even in the absence of clinical manifestations or metabolic syndrome, when there is an opportunity to modify/address risk factors and prevent disease progression. As such, they are relevant to patients, carers, clinicians, and policymakers.

PROTEUS Study: A Prospective Randomized Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiography.

BACKGROUND: Stress echocardiography (SE) is one of the most commonly used diagnostic imaging tests for coronary artery disease (CAD) but requires clinicians to visually assess scans to identify patients who may benefit from invasive investigation and treatment. EchoGo Pro provides an automated interpretation of SE based on artificial intelligence (AI) image analysis. In reader studies, use of EchoGo Pro when making clinical decisions improves diagnostic accuracy and confidence. Prospective evaluation in real world practice is now important to understand the impact of EchoGo Pro on the patient pathway and outcome. METHODS: PROTEUS is a randomized, multicenter, 2-armed, noninferiority study aiming to recruit 2,500 participants from National Health Service (NHS) hospitals in the UK referred to SE clinics for investigation of suspected CAD. All participants will undergo a stress echocardiogram protocol as per local hospital policy. Participants will be randomized 1:1 to a control group, representing current practice, or an intervention group, in which clinicians will receive an AI image analysis report (EchoGo Pro, Ultromics Ltd, Oxford, UK) to use during image interpretation, indicating the likelihood of severe CAD. The primary outcome will be appropriateness of clinician decision to refer for coronary angiography. Secondary outcomes will assess other health impacts including appropriate use of other clinical management approaches, impact on variability in decision making, patient and clinician qualitative experience and a health economic analysis. DISCUSSION: This will be the first study to assess the impact of introducing an AI medical diagnostic aid into the standard care pathway of patients with suspected CAD being investigated with SE. TRIAL REGISTRATION: Clinicaltrials.gov registration number NCT05028179, registered on 31 August 2021; ISRCTN: ISRCTN15113915; IRAS ref: 293515; REC ref: 21/NW/0199.

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers.

BACKGROUND: Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions. PURPOSE: To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region. STUDY TYPE: Cross-sectional. POPULATION: A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4). FIELD STRENGTH/SEQUENCE: A 1.5 T/balanced steady-state free precession. ASSESSMENT: An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49). STATISTICAL TEST: Multiple testing correction with false discovery method (threshold = 5%). RESULTS: The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10-6 ). DATA CONCLUSION: This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 1.

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction.

OBJECTIVES: To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank. METHODS: PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods. RESULTS: We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models. CONCLUSIONS: The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF. CLINICAL RELEVANCE STATEMENT: This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF. KEY POINTS: •PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

Prediction of incident cardiovascular events using machine learning and CMR radiomics.

OBJECTIVES: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS: • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases.

European Society of Cardiology: cardiovascular disease statistics 2021.

AIMS: This report from the European Society of Cardiology (ESC) Atlas Project updates and expands upon the widely cited 2019 report in presenting cardiovascular disease (CVD) statistics for the 57 ESC member countries. METHODS AND RESULTS: Statistics pertaining to 2019, or the latest available year, are presented. Data sources include the World Health Organization, the Institute for Health Metrics and Evaluation, the World Bank, and novel ESC sponsored data on human and capital infrastructure and cardiovascular healthcare delivery. New material in this report includes sociodemographic and environmental determinants of CVD, rheumatic heart disease, out-of-hospital cardiac arrest, left-sided valvular heart disease, the advocacy potential of these CVD statistics, and progress towards World Health Organization (WHO) 2025 targets for non-communicable diseases. Salient observations in this report: (i) Females born in ESC member countries in 2018 are expected to live 80.8 years and males 74.8 years. Life expectancy is longer in high income (81.6 years) compared with middle-income (74.2 years) countries. (ii) In 2018, high-income countries spent, on average, four times more on healthcare than middle-income countries. (iii) The median PM2.5 concentrations in 2019 were over twice as high in middle-income ESC member countries compared with high-income countries and exceeded the EU air quality standard in 14 countries, all middle-income. (iv) In 2016, more than one in five adults across the ESC member countries were obese with similar prevalence in high and low-income countries. The prevalence of obesity has more than doubled over the past 35 years. (v) The burden of CVD falls hardest on middle-income ESC member countries where estimated incidence rates are ∼30% higher compared with high-income countries. This is reflected in disability-adjusted life years due to CVD which are nearly four times as high in middle-income compared with high-income countries. (vi) The incidence of calcific aortic valve disease has increased seven-fold during the last 30 years, with age-standardized rates four times as high in high-income compared with middle-income countries. (vii) Although the total number of CVD deaths across all countries far exceeds the number of cancer deaths for both sexes, there are 15 ESC member countries in which cancer accounts for more deaths than CVD in males and five-member countries in which cancer accounts for more deaths than CVD in females. (viii) The under-resourced status of middle-income countries is associated with a severe procedural deficit compared with high-income countries in terms of coronary intervention, ablation procedures, device implantation, and cardiac surgical procedures. CONCLUSION: Risk factors and unhealthy behaviours are potentially reversible, and this provides a huge opportunity to address the health inequalities across ESC member countries that are highlighted in this report. It seems clear, however, that efforts to seize this opportunity are falling short and present evidence suggests that most of the WHO NCD targets for 2025 are unlikely to be met across ESC member countries.

Associations between moderate alcohol consumption, brain iron, and cognition in UK Biobank participants: Observational and mendelian randomization analyses.

BACKGROUND: Brain iron deposition has been linked to several neurodegenerative conditions and reported in alcohol dependence. Whether iron accumulation occurs in moderate drinkers is unknown. Our objectives were to investigate evidence in support of causal relationships between alcohol consumption and brain iron levels and to examine whether higher brain iron represents a potential pathway to alcohol-related cognitive deficits. METHODS AND FINDINGS: Observational associations between brain iron markers and alcohol consumption (n = 20,729 UK Biobank participants) were compared with associations with genetically predicted alcohol intake and alcohol use disorder from 2-sample mendelian randomization (MR). Alcohol intake was self-reported via a touchscreen questionnaire at baseline (2006 to 2010). Participants with complete data were included. Multiorgan susceptibility-weighted magnetic resonance imaging (9.60 ± 1.10 years after baseline) was used to ascertain iron content of each brain region (quantitative susceptibility mapping (QSM) and T2*) and liver tissues (T2*), a marker of systemic iron. Main outcomes were susceptibility (χ) and T2*, measures used as indices of iron deposition. Brain regions of interest included putamen, caudate, hippocampi, thalami, and substantia nigra. Potential pathways to alcohol-related iron brain accumulation through elevated systemic iron stores (liver) were explored in causal mediation analysis. Cognition was assessed at the scan and in online follow-up (5.82 ± 0.86 years after baseline). Executive function was assessed with the trail-making test, fluid intelligence with puzzle tasks, and reaction time by a task based on the "Snap" card game. Mean age was 54.8 ± 7.4 years and 48.6% were female. Weekly alcohol consumption was 17.7 ± 15.9 units and never drinkers comprised 2.7% of the sample. Alcohol consumption was associated with markers of higher iron (χ) in putamen (ß = 0.08 standard deviation (SD) [95% confidence interval (CI) 0.06 to 0.09], p < 0.001), caudate (ß = 0.05 [0.04 to 0.07], p < 0.001), and substantia nigra (ß = 0.03 [0.02 to 0.05], p < 0.001) and lower iron in the thalami (ß = -0.06 [-0.07 to -0.04], p < 0.001). Quintile-based analyses found these associations in those consuming >7 units (56 g) alcohol weekly. MR analyses provided weak evidence these relationships are causal. Genetically predicted alcoholic drinks weekly positively associated with putamen and hippocampus susceptibility; however, these associations did not survive multiple testing corrections. Weak evidence for a causal relationship between genetically predicted alcohol use disorder and higher putamen susceptibility was observed; however, this was not robust to multiple comparisons correction. Genetically predicted alcohol use disorder was associated with serum iron and transferrin saturation. Elevated liver iron was observed at just >11 units (88 g) alcohol weekly c.f. <7 units (56 g). Systemic iron levels partially mediated associations of alcohol intake with brain iron. Markers of higher basal ganglia iron associated with slower executive function, lower fluid intelligence, and slower reaction times. The main limitations of the study include that χ and T2* can reflect changes in myelin as well as iron, alcohol use was self-reported, and MR estimates can be influenced by genetic pleiotropy. CONCLUSIONS: To the best of our knowledge, this study represents the largest investigation of moderate alcohol consumption and iron homeostasis to date. Alcohol consumption above 7 units weekly associated with higher brain iron. Iron accumulation represents a potential mechanism for alcohol-related cognitive decline.

Higher spatial resolution improves the interpretation of the extent of ventricular trabeculation.

The ventricular walls of the human heart comprise an outer compact layer and an inner trabecular layer. In the context of an increased pre-test probability, diagnosis left ventricular noncompaction cardiomyopathy is given when the left ventricle is excessively trabeculated in volume (trabecular vol >25% of total LV wall volume) or thickness (trabecular/compact (T/C) >2.3). Here, we investigated whether higher spatial resolution affects the detection of trabeculation and thus the assessment of normal and excessively trabeculated wall morphology. First, we screened left ventricles in 1112 post-natal autopsy hearts. We identified five excessively trabeculated hearts and this low prevalence of excessive trabeculation is in agreement with pathology reports but contrasts the prevalence of approximately 10% of the population found by in vivo non-invasive imaging. Using macroscopy, histology and low- and high-resolution MRI, the five excessively trabeculated hearts were compared with six normal hearts and seven abnormally trabeculated and excessive trabeculation-negative hearts. Some abnormally trabeculated hearts could be considered excessively trabeculated macroscopically because of a trabecular outflow or an excessive number of trabeculations, but they were excessive trabeculation-negative when assessed with MRI-based measurements (T/C <2.3 and vol <25%). The number of detected trabeculations and T/C ratio were positively correlated with higher spatial resolution. Using measurements on high resolution MRI and with histological validation, we could not replicate the correlation between trabeculations of the left and right ventricle that has been previously reported. In conclusion, higher spatial resolution may affect the sensitivity of diagnostic measurements and in addition could allow for novel measurements such as counting of trabeculations.

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning.

BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. METHODS: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). FINDINGS: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. CONCLUSION: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision.

Evidence-based cardiovascular magnetic resonance cost-effectiveness calculator for the detection of significant coronary artery disease.

BACKGROUND: Although prior reports have evaluated the clinical and cost impacts of cardiovascular magnetic resonance (CMR) for low-to-intermediate-risk patients with suspected significant coronary artery disease (CAD), the cost-effectiveness of CMR compared to relevant comparators remains poorly understood. We aimed to summarize the cost-effectiveness literature on CMR for CAD and create a cost-effectiveness calculator, useable worldwide, to approximate the cost-per-quality-adjusted-life-year (QALY) of CMR and relevant comparators with context-specific patient-level and system-level inputs. METHODS: We searched the Tufts Cost-Effectiveness Analysis Registry and PubMed for cost-per-QALY or cost-per-life-year-saved studies of CMR to detect significant CAD. We also developed a linear regression meta-model (CMR Cost-Effectiveness Calculator) based on a larger CMR cost-effectiveness simulation model that can approximate CMR lifetime discount cost, QALY, and cost effectiveness compared to relevant comparators [such as single-photon emission computed tomography (SPECT), coronary computed tomography angiography (CCTA)] or invasive coronary angiography. RESULTS: CMR was cost-effective for evaluation of significant CAD (either health-improving and cost saving or having a cost-per-QALY or cost-per-life-year result lower than the cost-effectiveness threshold) versus its relevant comparator in 10 out of 15 studies, with 3 studies reporting uncertain cost effectiveness, and 2 studies showing CCTA was optimal. Our cost-effectiveness calculator showed that CCTA was not cost-effective in the US compared to CMR when the most recent publications on imaging performance were included in the model. CONCLUSIONS: Based on current world-wide evidence in the literature, CMR usually represents a cost-effective option compared to relevant comparators to assess for significant CAD.