Pericardial Disease in Cancer Patients

Purpose of review: To understand the variety of conditions in which the pericardium may be affected in cancer patients. // Recent findings: Cancer may affect the pericardium directly (primary cancer; uncommon) or through metastases (commoner). Cancer treatment (chemotherapy and radiotherapy) may affect the pericardium leading to pericarditis and myopericarditis. Pericardial effusions, tamponade and constrictive pericarditis are complications that can also occur. A variety of techniques (predominantly cardiac imaging related) are used to make the diagnosis with the treatment strategy dependent on whether the pericardial disease is due to cancer or as a result of cancer treatment. // Summary: A variety of pericardial diseases may be caused by cancer and cancer treatment. Determining the aetiology and providing effective treatment can often be challenging

Strut protrusion and shape impact on endothelial shear stress: insights from pre-clinical study comparing Mirage and Absorb bioresorbable scaffolds

Protrusion of scaffold struts is related with local coronary flow dynamics that can promote scaffold restenosis and thrombosis. That fact has prompted us to investigate in vivo the protrusion status of different types of scaffolds and their relationship with endothelial shear stress (ESS) distributions. Six Absorb everolimus-eluting Bioresorbable Vascular Scaffolds (Absorb, Abbott Vascular) and 11 Mirage sirolimus-eluting Bioresorbable Microfiber Scaffolds (Mirage, Manli Cardiology) were implanted in coronaries of eight mini pigs. Optical coherence tomography (OCT) was performed post-scaffold implantation and obtained images were fused with angiographic data to reconstruct the three dimensional coronary anatomy. Blood flow simulation was performed and ESS distribution was estimated for each scaffold. Protrusion distance was estimated using a dedicated software. Correlation between OCT-derived protrusion and ESS distribution was assessed for both scaffold groups. A significant difference was observed in the protrusion distances (156 ± 137 µm for Absorb, 139 ± 153 µm for Mirage; p = 0.035), whereas difference remained after adjusting the protrusion distances according to the luminal areas. Strut protrusion of Absorb is inversely correlated with ESS (r = -0.369, p < 0.0001), whereas in Mirage protrusion was positively correlated with EES (r = 0.192, p < 0.0001). Protrusion distance was higher in Absorb than in Mirage. The protrusion of the thick quadratic struts of Absorb has a tendency to lower shear stress in the close vicinity of struts. However, circular shape of the less thick struts of Mirage didn't show this trend in creating zone of recirculation around the struts. Strut geometry has different effect on the relationship between protrusion and shear stress in Absorb and Mirage scaffolds

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

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

Invasive or non-invasive imaging for detecting high-risk coronary lesions?

INTRODUCTION: Advances in our understanding about atherosclerotic evolution have enabled us to identify specific plaque characteristics that are associated with coronary plaque vulnerability and cardiovascular events. With constant improvements in signal and image processing an arsenal of invasive and non-invasive imaging modalities have been developed that are capable of identifying these features allowing in vivo assessment of plaque vulnerability. Areas covered: This review article presents the available and emerging imaging modalities introduced to assess plaque morphology and biology, describes the evidence from the first large scale studies that evaluated the efficacy of invasive and non-invasive imaging in detecting lesions that are likely to progress and cause cardiovascular events and discusses the potential implications of the in vivo assessment of coronary artery pathology in the clinical setting. Expert commentary: Invasive imaging, with its high resolution, and in particular hybrid intravascular imaging appears as the ideal approach to study the mechanisms regulating atherosclerotic disease progression; whereas non-invasive imaging is expected to enable complete assessment of coronary tree pathology, detection of high-risk lesions, more accurate risk stratification and thus to allow a personalized treatment of vulnerable patients

Effect of evidence-based therapy for secondary prevention of cardiovascular disease: Systematic review and meta-analysis

BACKGROUND: The combination pharmacotherapy of antiplatelet agents, lipid-modifiers, ACE inhibitors/ARBs and beta-blockers are recommended by international guidelines. However, data on effectiveness of the evidence-based combination pharmacotherapy (EBCP) is limited. OBJECTIVES: To determine the effect of EBCP on mortality and Cardiovascular events in patients with Coronary Heart Disease (CHD) or cerebrovascular disease. METHODS: Publications in EMBASE and Medline up to October 2018 were searched for cohort and case-control studies on EBCP for the secondary prevention of cardiovascular disease. The main outcomes were all-cause mortality and major cardiovascular events. Meta-analyses were performed based on random effects models. RESULTS: 21 studies were included. Comparing EBCP to either monotherapy or no therapy, the pooled risk ratios were 0.60 (95% confidence interval 0.55 to 0.66) for all-cause mortality, 0.70 (0.62 to 0.79) for vascular mortality, 0.73 (0.64 to 0.83) for myocardial infarction and 0.79 (0.68 to 0.91) for cerebrovascular events. Optimal EBCP (all 4 classes of drug prescribed) had a risk ratio for all-cause mortality of 0.50 (0.40 to 0.64). This benefit became more dilute as the number of different classes of drug comprising EBCP was decreased-for 3 classes of drug prescribed the risk ratio was 0.58 (0.49 to 0.69) and for 2 classes, the risk ratio was 0.67 (0.60 to 0.76). CONCLUSIONS: EBCP reduces the risk of all-cause mortality and cardiovascular events in patients with CHD or cerebrovascular disease. The different classes of drugs comprising EBCP work in an additive manner, with optimal EBCP conferring the greatest benefit

A randomized double-blind control study of early intra-coronary autologous bone marrow cell infusion in acute myocardial infarction: the REGENERATE-AMI clinical trial

Clinical trials suggest that intracoronary delivery of autologous bone marrow-derived cells (BMCs) 1–7 days post-acute myocardial infarction (AMI) may improve left ventricular (LV) function. Earlier time points have not been evaluated. We sought to determine the effect of intracoronary autologous BMC on LV function when delivered within 24 h of successful reperfusion therapy. Methods and results A multi-centre phase II randomized, double-blind, and placebo-controlled trial. One hundred patients with anterior AMI and significant regional wall motion abnormality were randomized to receive either intracoronary infusion of BMC or placebo (1:1) within 24 h of successful primary percutaneous intervention (PPCI). The primary endpoint was the change in left ventricular ejection fraction (LVEF) between baseline and 1 year as determined by advanced cardiac imaging. At 1 year, although LVEF increased compared with baseline in both groups, the between-group difference favouring BMC was small (2.2%; 95% confidence interval, CI: −0.5 to 5.0; P = 0.10). However, there was a significantly greater myocardial salvage index in the BMC-treated group compared with placebo (0.1%; 95% CI: 0.0–0.20; P = 0.048). Major adverse events were rare in both treatment groups. Conclusion The early infusion of intracoronary BMC following PPCI for patients with AMI and regional wall motion abnormality leads to a small non-significant improvement in LVEF when compared with placebo; however, it may play an important role in infarct remodelling and myocardial salvage.UK Stem Cells Foundation, the Heart Cells Foundation, and Barts and the London Charity. Funding to pay the Open Access publication charges for this article was provided by the Barts Cardiovascular Biomedical Research Unit (CVBRU)

Advanced Imaging Modalities to Monitor for Cardiotoxicity

OPINION STATEMENT: Early detection and treatment of cardiotoxicity from cancer therapies is key to preventing a rise in adverse cardiovascular outcomes in cancer patients. Over-diagnosis of cardiotoxicity in this context is however equally hazardous, leading to patients receiving suboptimal cancer treatment, thereby impacting cancer outcomes. Accurate screening therefore depends on the widespread availability of sensitive and reproducible biomarkers of cardiotoxicity, which can clearly discriminate early disease. Blood biomarkers are limited in cardiovascular disease and clinicians generally still use generic screening with ejection fraction, based on historical local expertise and resources. Recently, however, there has been growing recognition that simple measurement of left ventricular ejection fraction using 2D echocardiography may not be optimal for screening: diagnostic accuracy, reproducibility and feasibility are limited. Modern cancer therapies affect many myocardial pathways: inflammatory, fibrotic, metabolic, vascular and myocyte function, meaning that multiple biomarkers may be needed to track myocardial cardiotoxicity. Advanced imaging modalities including cardiovascular magnetic resonance (CMR), computed tomography (CT) and positron emission tomography (PET) add improved sensitivity and insights into the underlying pathophysiology, as well as the ability to screen for other cardiotoxicities including coronary artery, valve and pericardial diseases resulting from cancer treatment. Delivering screening for cardiotoxicity using advanced imaging modalities will however require a significant change in current clinical pathways, with incorporation of machine learning algorithms into imaging analysis fundamental to improving efficiency and precision. In the future, we should aspire to personalized rather than generic screening, based on a patient's individual risk factors and the pathophysiological mechanisms of the cancer treatment they are receiving. We should aspire that progress in cardiooncology is able to track progress in oncology, and to ensure that the current 'one size fits all' approach to screening be obsolete in the very near future

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images

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.Cardiovascular Aspects of Radiolog