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Spatial patterns of elevated wall shear stress and pressure due to blood flow past aortic stenosis (AS) are studied using GPU-accelerated patient-specific computational fluid dynamics. Three cases of moderate to severe AS, one with a dilated ascending aorta and two within the normal range (root diameter less than 4cm) are simulated for physiological waveforms obtained from echocardiography. The computational framework is built based on sharp-interface Immersed Boundary Method, where aortic geometries segmented from CT angiograms are integrated into a high-order incompressible Navier–Stokes solver. The key question addressed here is, given the presence of turbulence due to AS which increases wall shear stress (WSS) levels, why some AS patients undergo much less aortic dilation. Recent case studies of AS have linked the existence of an elevated WSS hotspot (due to impingement of AS on the aortic wall) to the dilation process. Herein we further investigate the WSS distribution for cases with and without dilation to understand the possible hemodynamics which may impact the dilation process. We show that the spatial distribution of elevated WSS is significantly more focused for the case with dilation than those without dilation. We further show that this focal area accommodates a persistent pocket of high pressure, which may have contributed to the dilation process through an increased wall-normal forcing. The cases without dilation, on the contrary, showed a rather oscillatory pressure behaviour, with no persistent pressure “buildup” effect. We further argue that a more proximal branching of the aortic arch could explain the lack of a focal area of elevated WSS and pressure, because it interferes with the impingement process due to fluid suction effects. These phenomena are further illustrated using an idealized aortic geometry. We finally show that a restored inflow eliminates the focal area of elevated WSS and pressure zone from the ascending aorta.

To evaluate bone marrow stem cell treatment (BMSC) in patients with ischemic heart disease (IHD) and no option of revascularization. Autologous BMSC therapy has emerged as a novel approach to treat patients with acute myocardial infarction or chronic ischemia and heart failure following percutaneous or surgical revascularization, respectively. However, the effect of the treatment has not been systematic evaluated in patients who are not eligible for revascularization. MEDLINE (1950-2012), EMBASE (1980-2012), CENTRAL (The Cochrane Library 2012, Issue 8) and ongoing trial databases were searched for relevant randomized controlled trials. Trials where participants were diagnosed with IHD, with no option for revascularization and who received any dose of stem cells by any delivery route were selected for inclusion. Study and participant characteristics, details of the intervention and comparator, and outcomes measured were recorded by two reviewers independently. Primary outcome measures were defined as mortality and measures of angina; secondary outcomes were heart failure, quality of life measures, exercise/performance and left ventricular ejection fraction (LVEF). Nine trials were eligible for inclusion. BMSC treatment significantly reduced the risk of mortality (Relative Risk 0.33; 95% Confidence Interval 0.17 to 0.65; P = 0.001). Patients who received BMSC showed a significantly greater improvement in CCS angina class (Mean Difference -0.55; 95% Confidence Interval -1.00 to -0.10; P = 0.02) and significantly fewer angina episodes per week at the end of the trial (Mean Difference -5.21; 95% Confidence Interval -7.35 to -3.07; P<0.00001) than those who received no BMSC. In addition, the treatment significantly improved quality of life, exercise/performance and LVEF in these patients. BMSC treatment has significant clinical benefit as stand-alone treatment in patients with IHD and no other treatment option. These results require confirmation in large well-powered trials with long-term follow-up to fully evaluate the clinical efficacy of this treatment.

In this Review, the authors present evidence for a causal relationship between the presence of large, dense, reactive platelets in the circulation and the onset of acute coronary syndromes (ACS). The increase in mean platelet volume in the pathogenesis of ACS can potentially overwhelm current therapeutics. Therefore, the control system for the production of these large platelets should be further researched to facilitate the development of new therapeutics that comprehensively prevent ACS. Platelets are causally involved in coronary artery obstruction in acute coronary syndromes (ACS). This cell type is unique to mammals and its production, which is unlike that of any other mammalian cell, involves polyploid nuclear change in the mother cell (megakaryocyte) and the production of anucleate cells with a log Gaussian distribution of volume. Platelets vary more in cellular volume than any other circulating blood element in mammals. Larger platelets are denser, contain more secretory granules, and are more reactive than their smaller counterparts. A causal relationship between the presence of large, dense, reactive platelets in the circulation and ACS is supported by many clinical studies. Furthermore, the results of two large, prospective, epidemiological studies have demonstrated that mean platelet volume was the strongest independent predictor of outcome in patients with acute myocardial infarction. Notably, evidence indicates that an increase in mean platelet volume in the pathogenesis of ACS can potentially overwhelm current therapeutics. The control system for the physiological and pathophysiological production of large platelets should, therefore, be researched. An understanding of this system might give rise to new therapeutics that could control platelet reactivity and thereby comprehensively prevent ACS. Key Points The megakaryocyte–platelet hemostatic system is unique to mammals, and has a complex control system that is not yet fully understood Larger platelets are denser and more reactive than smaller platelets, and are associated with megakaryocyte change Large clinical studies have shown a relationship between large platelets and incidence of acute coronary syndromes Two large prospective epidemiological studies have shown that the presence of large platelets can independently predict outcome related to acute myocardial infarction, including death Platelet physiological change has the potential to overwhelm current therapeutic interventions Future research should address the physiological and pathological production of large platelets

The blood pressure (BP) lowering response to renal denervation (RDN) remains variable with about one-third of patients not responding to ultrasound or radiofrequency RDN. Identification of predictors of the BP response to RDN is needed to optimize patient selection for this therapy. This is a post-hoc analysis of the RADIANCE-HTN SOLO study. BP response to RDN was measured by the change in daytime ambulatory systolic blood pressure (dASBP) at 2 months post procedure. Univariate regression was used initially to assess potential predictors of outcome followed by multivariate regression analysis. In the univariate analysis, predictors of response to RDN were higher baseline daytime ambulatory diastolic blood pressure (dADBP), the use of antihypertensive medications at screening, and presence of orthostatic hypertension (OHTN) whilst the presence of untreated accessory arteries was a negative predictor of response. Multivariate analysis determined that dADBP and use of antihypertensive medications were predictors of response to RDN with a trend for OHTN to predict response. Obese females also appeared to be better responders to RDN in an interaction model. RDN is more effective in patients with elevated baseline dADBP and those with OHTN, suggesting increased peripheral vascular resistance secondary to heightened sympathetic tone. These assessments are easy to perform in clinical setting and may help in phenotyping patients who will respond better to RDN.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.

Patient-specific digital simulation is emerging as a tool to support personalized planning of transcatheter aortic valve replacement (TAVR), particularly as the procedure expands to younger, lower-risk patients, and more complex anatomies. Despite procedural advances, complications such as paravalvular leak, conduction disturbances, coronary obstruction, and aortic injury remain important determinants of outcome. Current pre-procedural planning relies heavily on computed tomography-based anatomical assessment, which is indispensable but largely static and cannot fully capture dynamic device-tissue interactions, and haemodynamic mechanisms underlying many procedural events. Computational modelling derived from patient-specific imaging can extend this assessment by simulating valve deployment, device-tissue contact, and flow, offering mechanistic insight and potential support for individualized procedural decision-making. This systematic review evaluates modelling approaches addressing TAVR complications and procedural planning, including high-risk scenarios such as bicuspid valves and valve-in-valve procedures. Across the literature, modelling enables patient-specific simulations and exploration of procedural strategies that may reduce complication risk. However, clinical translation remains limited by small study populations, heterogeneous methodologies, limited patient-specific validation, and lack of integration into routine workflows. Future progress will require validation against clinically meaningful endpoints, scalable digital infrastructure, and close collaboration between clinicians and engineers to incorporate simulation outputs into routine Heart Team decision-making.

Cardiogenic shock is a life-threatening condition caused by the heart’s sudden inability to pump sufficient blood to maintain adequate tissue perfusion, most commonly occurring following a myocardial infarction or acute decompensated heart failure. The resultant hypoperfusion can quickly progress to end-organ failure and ultimately death if not treated urgently. This review explores the management of cardiogenic shock, highlighting current treatments, their effectiveness, and the challenges faced by healthcare providers. It looks at both pharmacological therapies and devices used for cardiac support, including mechanical circulatory support and emergency revascularisation procedures to restore blood flow. We also examine how different stages of shock affect survival and how new technologies including artificial intelligence and wearable monitors could help detect and treat this condition earlier. In addition, this review discusses the significant pressure that cardiogenic shock places on healthcare provision, including the typical financial cost of treatment in the UK, resource utilisation and regional disparities. Finally, we outline future directions for trial design, better prevention, more rapid diagnosis and improved treatments that could improve morbidity and mortality.

To investigate whether there are important sources of heterogeneity between the findings of different clinical trials which administer autologous stem cell treatment for acute myocardial infarction (AMI) and to evaluate what factors may influence the long-term effects of this treatment. MEDLINE (1950-January 2011), EMBASE (1974-January 2011), CENTRAL (The Cochrane Library 2011, Issue 1), CINAHL (1982-January 2011), and ongoing trials registers were searched for randomised trials of bone marrow stem cells as treatment for AMI. Hand-searching was used to screen recent, relevant conference proceedings (2005-2010/11). Meta-analyses were conducted using random-effects models and heterogeneity between subgroups was assessed using chi-squared tests. Planned analyses included length of follow-up, timing of cell infusion and dose, patient selection, small trial size effect, methodological quality, loss of follow-up and date of publication. Thirty-three trials with a total of 1,765 participants were included. There was no evidence of bias due to publication or time-lag, methodological quality of included studies, participant drop-out, duration of follow-up or date of the first disclosure of results. However, in long-term follow-ups the treatment seemed more effective when administered at doses greater than 10(8) cells and to patients with more severe heart dysfunction. Evaluation of heterogeneity between trials has not identified significant sources of bias in this study. However, clinical differences between trials are likely to exist which should be considered when undertaking future trials.

Fractional flow reserve is the gold standard for assessing the haemodynamic significance of intermediate coronary artery stenoses. Cumulative evidence has shown that FFR-guided revascularisation reduces stent implantations and improves patient outcomes. However, despite the wealth of evidence and guideline recommendations, its use in clinical practice remains minimal. Patient and technical limitations of FFR as well as the need for intracoronary instrumentation, use of adenosine, and increased costs have limited FFR’s applicability in clinical practice. Over the last decade, several angiography-derived FFR software packages have been developed which do not require intracoronary pressure assessment with a guidewire or need for administration of hyperaemic agents. At present, there are 3 commercially available software packages and several other non-commercial technologies that have been described in the literature. These technologies have been validated against invasive FFR showing good accuracy and correlation. However, the methodology behind these solutions is different—some algorithms are based on solving the governing equations of fluid dynamics such as the Navier–Stokes equation while others have opted for a more simplified mathematical formula approach. The aim of this review is to critically appraise the methodology behind all the known angiography-derived FFR technologies highlighting the key differences and limitations.

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding ( t -SNE) algorithm. The reduced dataset for t -SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on “unseen” meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.