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Background The clinical predictors and sequelae of leaflet thrombosis (LT) following transcatheter aortic valve replacement (TAVR) is still unclear. Therefore, our aim was to determine the clinical predictors and sequelae at mid-term follow-up of computed tomography (CT)-defined LT following TAVR. Methods and results We performed a prospective evaluation with a 320-multislice CT following TAVR for the presence of LT, defined as hypo-attenuated leaflet thickening (HALT). Four-dimensional CT image-rendering was performed to determine the presence of reduced leaflet motion (RELM). 172 patients [89 (51.7%) male, mean age 82.8 ± 5.7 years] treated with commercially available TAVR device (Lotus 54%, CoreValve 32% and Sapien 3 14%) were included, with median CT-scan at 6.0 weeks post-TAVR. Prevalence of HALT was 14.0% (24 cases) and RELM was 9.8% (17 cases). On multivariate analysis, patients with HALT were less prescribed oral anticoagulation (OAC) (OR 9.9), received larger TAVR prostheses (OR 5.7) and higher rates of moderate-severe para-valvular regurgitation (PVR) (OR 16.3). There was no difference in clinical outcomes at a median follow-up of 2.3 years. Patients with RELM had significantly higher transvalvular gradients after discharge when compared to those without RELM. Conclusions Absence of OAC, large TAVR prostheses and moderate-severe PVR were predictors for LT. Transvalvular gradients were higher in patients that developed RELM but not HALT. Further studies are warranted to determine the long-term impact of LT on TAVR durability. Graphic abstract Prevalence of different sub-types of CT-defined LT (HALT and RELM) and the clinical predictors of developing LT following TAVR. CT computed tomography, HALT hypo-attenuated leaflet thickening, LT leaflet thrombosis, RELM reduced leaflet motion, TAVR transcatheter aortic valve replacement

The manufacture of artificial heart valves is very challenging owing to their stringent functional requirements.The latest advances in 3D printing technologies hold great promise to revolutionise the manufacture of artificial heart valves.Recent 3D printing innovations go far beyond the fabrication of anatomical moulds, and unlock unprecedented opportunities to manufacture exquisite architectures, hierarchical bioprinting of cells, complex multi-material assembly, and the creation of biomimetic structures.This provides new avenues to enhance the functionality of artificial heart valves with respect to hydrodynamics, durability, biocompatibility, and the ability to be implanted via a minimally invasive transcatheter procedure. 3D printing technologies have the potential to revolutionize the manufacture of heart valves through the ability to create bespoke, complex constructs. In light of recent technological advances, we review the progress made towards 3D printing of heart valves, focusing on studies that have utilised these technologies beyond manufacturing patient-specific moulds. We first overview the key requirements of a heart valve to assess functionality. We then present the 3D printing technologies used to engineer heart valves. By referencing International Organisation for Standardisation (ISO) Standard 5840 (Cardiovascular implants – Cardiac valve prostheses), we provide insight into the achieved functionality of these valves. Overall, 3D printing promises to have a significant positive impact on the creation of artificial heart valves and potentially unlock full complex functionality. 3D printing technologies have the potential to revolutionize the manufacture of heart valves through the ability to create bespoke, complex constructs. In light of recent technological advances, we review the progress made towards 3D printing of heart valves, focusing on studies that have utilised these technologies beyond manufacturing patient-specific moulds. We first overview the key requirements of a heart valve to assess functionality. We then present the 3D printing technologies used to engineer heart valves. By referencing International Organisation for Standardisation (ISO) standard 5840 (cardiac valve prostheses), we provide insight into the achieved functionality of these valves. Overall, 3D printing promises to have a significant positive impact on the creation of artificial heart valves and potentially unlock full complex functionality.

ObjectivesTo obtain more powerful assessment of the prognostic value of fractional flow reserveCT testing we performed a systematic literature review and collaborative meta-analysis of studies that assessed clinical outcomes of CT-derived calculation of FFR (FFRCT) (HeartFlow) analysis in patients with stable coronary artery disease (CAD).MethodsWe searched PubMed and Web of Science electronic databases for published studies that evaluated clinical outcomes following fractional flow reserveCT testing between 1 January 2010 and 31 December 2020. The primary endpoint was defined as ‘all-cause mortality (ACM) or myocardial infarction (MI)’ at 12-month follow-up. Exploratory analyses were performed using major adverse cardiovascular events (MACEs, ACM+MI+unplanned revascularisation), ACM, MI, spontaneous MI or unplanned (>3 months) revascularisation as the endpoint.ResultsFive studies were identified including a total of 5460 patients eligible for meta-analyses. The primary endpoint occurred in 60 (1.1%) patients, 0.6% (13/2126) with FFRCT>0.80% and 1.4% (47/3334) with FFRCT ≤0.80 (relative risk (RR) 2.31 (95% CI 1.29 to 4.13), p=0.005). Likewise, MACE, MI, spontaneous MI or unplanned revascularisation occurred more frequently in patients with FFRCT ≤0.80 versus patients with FFRCT >0.80. Each 0.10-unit FFRCT reduction was associated with a greater risk of the primary endpoint (RR 1.67 (95% CI 1.47 to 1.87), p<0.001).ConclusionsThe 12-month outcomes in patients with stable CAD show low rates of events in those with a negative FFRCT result, and lower risk of an unfavourable outcome in patients with a negative test result compared with patients with a positive test result. Moreover, the FFRCT numerical value was inversely associated with outcomes.

Fractional flow reserve (FFR) is routinely used to determine lesion severity prior to percutaneous coronary intervention (PCI). However, there is an increasing recognition that FFR may also be useful following PCI to identify mechanisms leading to restenosis and the need for repeat revascularization. Post-PCI FFR is associated with the presence and severity of stent under-expansion and may help identify peri-stent-related complications. FFR pullback may also unmask other functionally significant lesions within the target vessel that were not appreciable on angiography. Recent studies have confirmed the prognostic utility of performing routine post-PCI FFR and suggest possible interventional targets that would improve stent durability. In this review, we detail the theoretical basis underlying post-PCI FFR, provide practical tips to facilitate measurement, and discuss the growing evidence supporting its use.

•Left ventricular (LV) thrombus is often treated using DOACs, but with limited randomized evidence (85 characters).•The RELEVENT trial tests the noninferiority of DOACs to warfarin for the treatment of LV thrombus (84 characters).•The primary outcome of the RELEVENT trial is thrombus resolution without adverse clinical events (84 characters).•The RELEVENT trial will inform clinicians and guidelines on the optimal management of LV thrombus (84 characters). Left ventricular (LV) thrombus is a consequence of systolic dysfunction and is associated with an increased risk of stroke and systemic embolism. Anticoagulation with warfarin has been the standard of care. However, following the widespread adoption of direct oral anticoagulants (DOACs) in other settings, these are increasingly used to treat LV thrombus, despite limited randomized data to support equivalent outcomes and safety. We hypothesize that DOACs will be noninferior to warfarin in the resolution of LV thrombus, without the occurrence of cardiovascular death, stroke, systemic embolism or major bleeding at 3-months. The REsolution of LEft VENTricular thrombus (RELEVENT) trial (ACTRN12618001254280) will test the noninferiority of DOACs compared to warfarin. This prospective trial will randomize 216 patients with best-available imaging confirmed LV thrombus, at a 1:1 ratio to either warfarin or a DOAC, for a duration of 12 to 14 weeks. Any DOAC approved for stroke prevention in atrial fibrillation may be used, according to local preference. The primary endpoint will be the resolution of the thrombus, without the occurrence of cardiovascular death, stroke, systemic embolism or major bleeding at 3-month follow-up. Secondary and other endpoints of interest include components of the primary outcome, changes to thrombus diameter, days alive and out of hospital, disability free survival and quality of life. Patients will be followed up for 3 years to obtain data on long-term management and outcomes. Recruitment to the RELEVENT trial is underway in 16 centers in New Zealand and Australia. The RELEVENT trial will help clarify whether DOACs are noninferior to warfarin in the early treatment of LV thrombus. It will also generate important insights into the long-term management and outcomes for patients. [Display omitted]

Objectives Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. Methods Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1–10, 11–100, 101–400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). Results Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers ( p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman’s r = 0.90, 95% confidence interval [CI] 0.89–0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98–0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland–Altman analysis demonstrated little difference (1.69, 95% limits of agreement: −41.22, 44.60) and there was almost excellent agreement (Cohen’s κ = 0.90, 95% CI 0.88–0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. Conclusions This artificial intelligence–based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. Key Points • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)–based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

Patients with ST-elevation myocardial infarction (STEMI) with no standard modifiable risk factors (SMuRFs: hypertension, diabetes mellitus, hypercholesterolemia, and smoking) have worse short-term mortality than those with SMuRFs. Whether this association extends to younger patients is unclear. A retrospective cohort study was performed of patients aged 18 to 45 years with STEMI at 3 Australian hospitals between 2010 and 2020. Nonatherosclerotic causes of STEMI were excluded. The primary outcome was 30-day all-cause mortality. Secondary outcomes included 1 and 2-year mortality. Cox proportional hazards analysis was used. Of 597 patients, the median age was 42 (interquartile range 38 to 44) years, 85.1% were men and 8.4% were SMuRF-less. Patients who are SMuRF-less were >2 times more likely to have cardiac arrest (28.0% vs 12.6%, p = 0.003); require vasopressors (16.0% vs 6.8%, p = 0.018), mechanical support (10.0% vs 2.3%, p = 0.046), or intensive care admission (20.0% vs 5.7%, p <0.001); and have higher rate of left anterior descending artery infarcts than those with SMuRFs (62.0% vs 47.2%, p = 0.045). No significant differences in thrombolysis or percutaneous intervention were observed. Guideline-directed medical therapy at discharge was high (>90%), and not different in the SMuRF-less. 30-day mortality was almost fivefold higher in the SMuRF-less (hazard ratio 4.70, 95% confidence interval 1.66 to 13.35, p = 0.004), remaining significant at 1 and 2 years. In conclusion, young patients who are SMuRF-less have a higher 30-day mortality after STEMI than their counterparts with SMuRFs. This may be partially mediated by higher rates of cardiac arrest and left anterior descending artery territory events. These findings further highlight the need for improved prevention and management of SMuRF-less STEMI.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia, with catheter ablation being a key alternative to medical treatment for restoring normal sinus rhythm. Despite advances in understanding AF pathogenesis, approximately 35% of patients experience AF recurrence at 12 months after catheter ablation. Therefore, accurate prediction of AF recurrence occurring after catheter ablation is important for patient selection and management. Conventional methods for predicting post-catheter ablation AF recurrence, which involve the use of univariate predictors and scoring systems, have played a supportive role in clinical decision-making. In an ever-changing landscape where technology is becoming ubiquitous within medicine, cardiac imaging and artificial intelligence (AI) could prove pivotal in enhancing AF recurrence predictions by providing data with independent predictive power and identifying key relationships in the data. This review comprehensively explores the existing methods for predicting the recurrence of AF following catheter ablation from different perspectives, including conventional predictors and scoring systems, cardiac imaging-based methods, and AI-based methods developed using a combination of demographic and imaging variables. By summarising state-of-the-art technologies, this review serves as a roadmap for developing future prediction models with enhanced accuracy, generalisability, and explainability, potentially contributing to improved care for patients with AF.