Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study.

Aims: Automated algorithms are regularly used to analyse cardiac magnetic resonance (CMR) images. Validating data output reliability from this method is crucial for enabling widespread adoption. We outline a visual quality control (VQC) process for image analysis using automated batch processing. We assess the performance of automated analysis and the reliability of replacing visual checks with statistical outlier (SO) removal approach in UK Biobank CMR scans. Methods and results: We included 1987 CMR scans from the UK Biobank COVID-19 imaging study. We used batch processing software (Circle Cardiovascular Imaging Inc.-CVI42) to automatically extract chamber volumetric data, strain, native T1, and aortic flow data. The automated analysis outputs (∼62 000 videos and 2000 images) were visually checked by six experienced clinicians using a standardized approach and a custom-built R Shiny app. Inter-observer variability was assessed. Data from scans passing VQC were compared with a SO removal QC method in a subset of healthy individuals (n = 1069). Automated segmentation was highly rated, with over 95% of scans passing VQC. Overall inter-observer agreement was very good (Gwet's AC2 0.91; 95% confidence interval 0.84, 0.94). No difference in overall data derived from VQC or SO removal in healthy individuals was observed. Conclusion: Automated image analysis using CVI42 prototypes for UK Biobank CMR scans demonstrated high quality. Larger UK Biobank data sets analysed using these automated algorithms do not require in-depth VQC. SO removal is sufficient as a QC measure, with operator discretion for visual checks based on population or research objectives.

Health data science course for clinicians: Time to bridge the skills gap?

BACKGROUND: Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. METHODS: A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. RESULTS: The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p = .0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p = .0001, on a 5-point Likert scale). CONCLUSION: This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education.

How to Conduct Clinical Research in Cardiovascular Imaging: A Primer for Clinical Cardiologists and Researchers. A Statement of the European Association of Cardiovascular Imaging (EACVI) of the ESC.

This statement from the European Association of Cardiovascular Imaging (EACVI) of the ESC aims to address the fundamental principles that guide clinical research in the field of cardiovascular imaging. It provides clinical researchers, cardiology fellows, and Ph.D. students with a condensed, updated, and practical reference document to support them in designing, implementing, and conducting imaging protocols for clinical trials. Although the present article cannot replace formal research training and mentoring, it is recommended reading for any professional interested in becoming acquainted with or participating in clinical trials involving cardiovascular imaging.

Predictive Performance of Cardiovascular Risk Scores in Cancer Survivors From the UK Biobank.

Background: Cardiovascular preventive strategies are guided by risk scores with unknown validity in cancer cohorts. Objectives: This study aimed to evaluate the predictive performance of 7 established cardiovascular risk scores in cancer survivors from the UK Biobank. Methods: The predictive performance of QRISK3, Systematic Coronary Risk Evaluation 2 (SCORE2)/Systematic Coronary Risk Evaluation for Older Persons (SCORE-OP), Framingham Risk Score, Pooled Cohort equations to Prevent Heart Failure (PCP-HF), CHARGE-AF, QStroke, and CHA2DS2-VASc was calculated in participants with and without a history of cancer. Participants were propensity matched on age, sex, deprivation, health behaviors, family history, and metabolic conditions. Analyses were stratified into any cancer, breast, lung, prostate, brain/central nervous system, hematologic malignancies, Hodgkin lymphoma, and non-Hodgkin lymphoma. Incident cardiovascular events were tracked through health record linkage over 10 years of follow-up. The area under the receiver operating curve, balanced accuracy, and sensitivity were reported. Results: The analysis included 31,534 cancer survivors and 126,136 covariate-matched controls. Risk score distributions were near identical in cases and controls. Participants with any cancer had a significantly higher incidence of all cardiovascular outcomes than matched controls. Performance metrics were significantly worse for all risk scores in cancer cases than in matched controls. The most notable differences were among participants with a history of hematologic malignancies who had significantly higher outcome rates and poorer risk score performance than their matched controls. The performance of risk scores for predicting stroke in participants with brain/central nervous system cancer was very poor, with predictive accuracy more than 30% lower than noncancer controls. Conclusions: Existing cardiovascular risk scores have significantly worse predictive accuracy in cancer survivors compared with noncancer comparators, leading to an underestimation of risk in this cohort.

Imaging for the diagnosis of acute myocarditis: can artificial intelligence improve diagnostic performance?

Myocarditis is a cardiovascular disease characterised by inflammation of the heart muscle which can lead to heart failure. There is heterogeneity in the mode of presentation, underlying aetiologies, and clinical outcome with impact on a wide range of age groups which lead to diagnostic challenges. Cardiovascular magnetic resonance (CMR) is the preferred imaging modality in the diagnostic work-up of those with acute myocarditis. There is a need for systematic analytical approaches to improve diagnosis. Artificial intelligence (AI) and machine learning (ML) are increasingly used in CMR and has been shown to match human diagnostic performance in multiple disease categories. In this review article, we will describe the role of CMR in the diagnosis of acute myocarditis followed by a literature review on the applications of AI and ML to diagnose acute myocarditis. Only a few papers were identified with limitations in cases and control size and a lack of detail regarding cohort characteristics in addition to the absence of relevant cardiovascular disease controls. Furthermore, often CMR datasets did not include contemporary tissue characterisation parameters such as T1 and T2 mapping techniques, which are central to the diagnosis of acute myocarditis. Future work may include the use of explainability tools to enhance our confidence and understanding of the machine learning models with large, better characterised cohorts and clinical context improving the diagnosis of acute myocarditis.

The Association Between Periodontal Disease and Cardiovascular Disease: Insights From Imaging, Observational, and Genetic Data.

Background: Periodontal disease is the sixth most common disease worldwide and may be a contributory risk factor for cardiovascular disease (CVD). Objectives: This study utilizes noninvasive cardiac imaging and longitudinal and genetic data to characterize the association between periodontal disease and both cardiovascular magnetic resonance (CMR) imaging biomarkers of remodeling and incident coronary artery disease (CAD). Methods: From the UK Biobank, 481,915 individuals were included, 91,022 (18.9%) of whom had self-reported periodontal disease. For imaging analysis, 59,019 had paired CMR data. Multivariable linear regression models were constructed to examine the association of periodontal disease on CMR outcomes. The endpoints for the CMR analyses were left ventricle (LV) end-diastolic volume, LV ejection fraction, LV mass, LV mass:volume ratio, LV global longitudinal strain, and native T1 values. The relationship between periodontal disease and CVD was assessed using Cox proportional hazards regression models, with incident CAD as the endpoint. To examine the relationship of genetically determined periodontal disease on CAD, a genome-wide polygenic risk score was constructed. Results: Periodontal disease was associated with a significantly higher LV mass:volume ratio (effect size: 0.00233; 95% CI: 0.0006-0.004) and significantly lower T1 values (effect size: -0.86 ms; 95% CI: -1.63 to -0.09). Periodontal disease was independently associated with an increased hazard of incident CAD (HR: 1.09; 95% CI: 1.07-1.13) at a median follow-up time of 13.8 years. Each SD increase in the periodontal disease polygenic risk score was associated with increased odds of CAD (OR: 1.03; 95% CI: 1.02-1.05). Conclusions: Using an integrated approach across imaging, observational, and genomic data, periodontal disease is associated with biomarkers of subclinical remodeling as well as incident CAD. These findings highlight the potential importance of periodontal disease in the broader context of CVD prevention.

A review of evaluation approaches for explainable AI with applications in cardiology.

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-024-10852-w.

Management of acute myocarditis: a systematic review of clinical practice guidelines and recommendations.

The management of acute myocarditis (AM) is addressed in multiple clinical guidelines. We systematically reviewed current guidelines developed by national and international medical organizations on the management of AM to aid clinical practice. Publications in MEDLINE, EMBASE and Cochrane were identified between 1 January 2013 and 12 April 2024. Additionally, the websites of relevant organizations and the Guidelines International Network, Guideline Central, and NHS knowledge and library hub were reviewed. Two reviewers independently screened titles and abstracts, two reviewers assessed the rigour of guideline development, and one reviewer extracted the recommendations. Two of the three guidelines identified showed good rigour of development. Those rigorously developed agreed on the definition of AM, sampling serum troponin as part of the workflow for AM, testing for B-type natriuretic peptides in heart failure, key diagnostic imaging in the form of cardiovascular magnetic resonance, coronary angiography to exclude significant coronary disease, indications for endomyocardial biopsy (EMB), and indications for immunosuppression and advanced treatment options. Discrepancies exist in sampling creatine kinase-myocardial bound as a marker of myocardial injury, indications for EMB, and indications for immunosuppression and treatment of uncomplicated AM. Evidence is lacking for the use of 18F-Fluorodeoxyglucose Positron Emission Tomography for myocardial imaging, exercise restriction, follow-up measures and genetic testing, and there are few high-quality randomized trials to support treatment recommendations. Recommendations for management of AM in the guidelines have largely been developed from expert opinion rather than trial data.

Prevalence, Cardiac Phenotype, and Outcomes of Transthyretin Variants in the UK Biobank Population.

Importance: The population prevalence of cardiac transthyretin amyloidosis (ATTR) caused by pathogenic variation in the TTR gene (vATTR) is unknown. Objective: To estimate the population prevalence of disease-causing TTR variants and evaluate associated phenotypes and outcomes. Design, Setting, and Participants: This population-based cohort study analyzed UK Biobank (UKB) participants with whole-exome sequencing, electrocardiogram, and cardiovascular magnetic resonance data. Participants were enrolled from 2006 to 2010, with a median follow-up of 12 (IQR, 11-13) years (cutoff date for the analysis, March 12, 2024). Sixty-two candidate TTR variants were extracted based on rarity (minor allele frequency ≤0.0001) and/or previously described associations with amyloidosis if more frequent. Exposure: Carrier status for TTR variants. Main Outcomes and Measures: Associations of TTR carrier status with vATTR prevalence and cardiovascular imaging and electrocardiogram traits were explored using descriptive statistics. Associations between TTR carrier status and atrial fibrillation, conduction disease, heart failure, and all-cause mortality were evaluated using adjusted Cox proportional hazards models. Genotypic and diagnostic concordance was examined using International Statistical Classification of Diseases, Tenth Revision codes from the hospital record. Results: The overall cohort included 469 789 UKB participants (mean [SD] age, 56.5 [8.1] years; 54.2% female and 45.8% male). A likely pathogenic/pathogenic (LP/P) TTR variant was detected in 473 (0.1%) participants, with Val142Ile being the most prevalent (367 [77.6%]); 91 individuals (0.02%) were carriers of a variant of unknown significance . The overall prevalence of LP/P variants was 0.02% (105 of 444 243) in participants with European ancestry and 4.3% (321 of 7533) in participants with African ancestry. The LP/P variants were associated with higher left ventricular mass indexed to body surface area (ß = 4.66; 95% CI, 1.87-7.44), and Val142Ile was associated with a longer PR interval (ß = 18.34; 95% CI, 5.41-31.27). The LP/P carrier status was associated with a higher risk of heart failure (hazard ratio [HR], 2.68; 95% CI, 1.75-4.12) and conduction disease (HR, 1.88; 95% CI, 1.25-2.83). Higher all-cause mortality risk was observed for non-Val142Ile LP/P variants (HR, 1.98; 95% CI, 1.06-3.67). Thirteen participants (2.8%) with LP/P variants had diagnostic codes compatible with cardiac or neurologic amyloidosis. Variants of unknown significance were not associated with outcomes. Conclusions and Relevance: This study found that approximately 1 in 1000 UKB participants were LP/P TTR variant carriers, exceeding previously reported prevalence. The findings emphasize the need for clinical vigilance in identifying individuals at risk of developing vATTR and associated poor outcomes.

Risk factors for raised left ventricular filling pressure by cardiovascular magnetic resonance: Prognostic insights.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging shows promise in estimating pulmonary capillary wedge pressure (PCWP) non-invasively. At the population level, the prognostic role of CMR-modelled PCWP remains unknown. Furthermore, the relationship between CMR-modelled PCWP and established risk factors for cardiovascular disease has not been well characterized. OBJECTIVE: The main aim of this study was to investigate the prognostic value of CMR-modelled PCWP at the population level. METHODS: Employing data from the imaging substudy of the UK Biobank, a very large prospective population-based cohort study, CMR-modelled PCWP was calculated using a model incorporating left atrial volume, left ventricular mass and sex. Logistic regression explored the relationships between typical cardiovascular risk factors and raised CMR-modelled PCWP (≥15 mmHg). Cox regression was used to examine the impact of typical risk factors and CMR-modelled PCWP on heart failure (HF) and major adverse cardiovascular events (MACE). RESULTS: Data from 39 163 participants were included in the study. Median age of all participants was 64 years (inter-quartile range: 58 to 70), and 47% were males. Clinical characteristics independently associated with raised CMR-modelled PCWP included hypertension [odds ratio (OR) 1.57, 95% confidence interval (CI) 1.44-1.70, P < 0.001], body mass index (BMI) [OR 1.57, 95% CI 1.52-1.62, per standard deviation (SD) increment, P < 0.001], male sex (OR 1.37, 95% CI 1.26-1.47, P < 0.001), age (OR 1.33, 95% CI 1.27-1.41, per decade increment, P < 0.001) and regular alcohol consumption (OR 1.10, 95% CI 1.02-1.19, P = 0.012). After adjusting for potential confounders, CMR-modelled PCWP was independently associated with incident HF [hazard ratio (HR) 2.91, 95% CI 2.07-4.07, P < 0.001] and MACE (HR 1.48, 95% CI 1.16-1.89, P = 0.002). CONCLUSIONS: Raised CMR-modelled PCWP is an independent risk factor for incident HF and MACE. CMR-modelled PCWP should be incorporated into routine CMR reports to guide HF diagnosis and further management.

Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction.

Aims: Disruption of the predictable symmetry of the healthy heart may be an indicator of cardiovascular risk. This study defines the population distribution of ventricular asymmetry and its relationships across a range of prevalent and incident cardiorespiratory diseases. Methods and results: The analysis includes 44 796 UK Biobank participants (average age 64.1 ± 7.7 years; 51.9% women). Cardiovascular magnetic resonance (CMR) metrics were derived using previously validated automated pipelines. Ventricular asymmetry was expressed as the ratio of left and right ventricular (LV and RV) end-diastolic volumes. Clinical outcomes were defined through linked health records. Incident events were those occurring for the first time after imaging, longitudinally tracked over an average follow-up time of 4.75 ± 1.52 years. The normal range for ventricular symmetry was defined in a healthy subset. Participants with values outside the 5th-95th percentiles of the healthy distribution were classed as either LV dominant (LV/RV > 112%) or RV dominant (LV/RV < 80%) asymmetry. Associations of LV and RV dominant asymmetry with vascular risk factors, CMR features, and prevalent and incident cardiovascular diseases (CVDs) were examined using regression models, adjusting for vascular risk factors, prevalent diseases, and conventional CMR measures. Left ventricular dominance was linked to an array of pre-existing vascular risk factors and CVDs, and a two-fold increased risk of incident heart failure, non-ischaemic cardiomyopathies, and left-sided valvular disorders. Right ventricular dominance was associated with an elevated risk of all-cause mortality. Conclusion: Ventricular asymmetry has clinical utility for cardiovascular risk assessment, providing information that is incremental to traditional risk factors and conventional CMR metrics.

Myocardial Strain Measured by Cardiac Magnetic Resonance Predicts Cardiovascular Morbidity and Death.

BACKGROUND: Myocardial strain using cardiac magnetic resonance (CMR) is a sensitive marker for predicting adverse outcomes in many cardiac disease states, but the prognostic value in the general population has not been studied conclusively. OBJECTIVES: The goal of this study was to assess the independent prognostic value of CMR feature tracking (FT)-derived LV global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) metrics in predicting adverse outcomes (heart failure, myocardial infarction, stroke, and death). METHODS: Participants from the UK Biobank population imaging study were included. Univariable and multivariable Cox models were used for each outcome and each strain marker (GLS, GCS, GRS) separately. The multivariable models were tested with adjustment for prognostically important clinical features and conventional global LV imaging markers relevant for each outcome. RESULTS: Overall, 45,700 participants were included in the study (average age 65 ± 8 years), with a median follow-up period of 3 years. All univariable and multivariable models demonstrated that lower absolute GLS, GCS, and GRS were associated with increased incidence of heart failure, myocardial infarction, stroke, and death. All strain markers were independent predictors (incrementally above some respective conventional LV imaging markers) for the morbidity outcomes, but only GLS predicted death independently: (HR: 1.18; 95% CI: 1.07-1.30). CONCLUSIONS: In the general population, LV strain metrics derived using CMR-FT in radial, circumferential, and longitudinal directions are strongly and independently predictive of heart failure, myocardial infarction, and stroke, but only GLS is independently predictive of death in an adult population cohort.

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.

Multimodality imaging for the evaluation and management of patients with long-term (durable) left ventricular assist devices.

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 of these patients, these are limited by peri-operative and post-operative complications, such as device-related infections, haemocompatibility-related events, device mis-positioning, 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 LVAD candidates and the management LVAD 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 LVAD implantation.

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.

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.

Global longitudinal active strain energy density (GLASED): a powerful prognostic marker in a community-based cohort.

AIMS: 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 (LV) myocardial mass, ejection fraction, myocardial strain, stroke work, contraction fraction, pressure-strain product, and a new measurement called global longitudinal active strain density (GLASED). This study sought to compare the utility of 23 potential LV prognostic markers of structure and contractile function in a community-based cohort. METHODS AND RESULTS: The impact of cardiovascular magnetic resonance image-derived markers extracted by machine learning algorithms was compared with the future risk of adverse events in a group of 44 957 UK Biobank participants. Most markers, including the LV 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). CONCLUSION: GLASED predicted all-cause mortality and major cardiovascular adverse events better than conventional markers of risk and is recommended for assessing patient prognosis.

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.