Cardiovascular computed tomography imaging for coronary artery disease risk: plaque, flow and fat

Cardiac imaging is central to the diagnosis and risk stratification of coronary artery disease, beyond symptoms and clinical risk factors, by providing objective evidence of myocardial ischaemia and characterisation of coronary artery plaque. CT coronary angiography can detect coronary plaque with high resolution, estimate the degree of functional stenosis and characterise plaque features. However, coronary artery disease risk is also driven by biological processes, such as inflammation, that are not fully reflected by severity of stenosis, myocardial ischaemia or by coronary plaque features. New cardiac CT techniques can assess coronary artery inflammation by imaging perivascular fat, and this may represent an important step forward in identifying the ‘residual risk’ that is not detected by plaque or ischaemia imaging. Coronary artery disease risk assessment that incorporates clinical factors, plaque characteristics and perivascular inflammation offers a more comprehensive individualised approach to quantify and stratify coronary artery disease risk, with potential healthcare benefits for prevention, diagnosis and treatment recommendations. Furthermore, identifying new biomarkers of cardiovascular risk has the potential to refine early-life prevention strategies, before atherosclerosis becomes established.

Evaluation of the Efficacy of Statins in the Treatment of Coronary Artery Plaque Using Dual-Source Spiral Computed Tomography Image Features under Deep Learning

This study aimed at discussing deep learning-based dual-source spiral computed tomography (DSCT) image in the evaluation of the efficacy of statins in the treatment of coronary artery plaque. A convolutional neural network (CNN) algorithm was proposed in this study. On this basis, the model was improved, the Res-Net network was applied to reconstruct the computed tomography (CT) image, and the deep learning network model Mask R-CNN was constructed to enhance the ability of image reconstruction. Then, 80 patients with coronary artery disease who were treated in hospital were selected as the research objects and divided into a control group (n = 40) and an observation group (n = 40). There were 21 male patients and 19 female patients in the control group, with an average age of 52 ± 3.2 years; there were 24 male patients and 16 female patients in the observation group, with an average age of 51 ± 2.4 years. The observation group was reconstructed with the constructed model, and patients in the control group received traditional CT. The interval between two examinations was 6–12 months, with an average interval of 8 ± 1.78 months. During the interval, all patients received conservative treatment mainly with atorvastatin. The general data of the two groups were comparable without statistical significance ( P > 0.05 ). A network model was constructed to measure the coronary plaque and vascular volume of the patients, and the images were reconstructed on the Res-Net network. The loss value of Res-Net network was stable at the lowest level around 0.02, showing a very fast effect in the training process. After statin treatment, the vascular volume and coronary plaque volume of the patients were decreased obviously ( P < 0.05 ). The average time spent in the network model was 1.20 seconds. The average time spent in the measurement of each disc by doctors A, B, and C was 186 seconds, 158 seconds, and 142 seconds, respectively. The construction of network model markedly improved the speed of CT image diagnosis and treatment. In conclusion, the Res-Net network model proposed in this study had certain feasibility and effectiveness for dual-source CT (DSCT) image segmentation and could effectively improve the clinical information evaluation of CT images from patients with coronary artery disease, which had important reference value for the development of intelligent medical equipment. It could provide a new diagnostic method for clinical prediction and diagnosis of coronary artery disease (CAD).

Effects of statin therapy on coronary plaque volume by decreasing CRP/hsCRP levels: A meta-regression of randomized controlled trials

Background and aims: Several clinical trials have indicated that statins stabilize and reverse atherosclerotic plaque. However, different studies have provided inconsistent findings regarding mechanisms and influencing factors of plaque regression under statin therapy. In this study, meta-analysis and meta-regression were used to determine the effect of statin medication on coronary plaque volume as determined by intravenous ultrasound. Meanwhile, the impact of statins on CRP/hsCRP reduction on plaque regression was discussed. Methods: Up to May 28, 2021, a systematic PubMed, EMBASE, and Cochrane search was performed for randomized controlled trials that assessed treatment effect using total atheroma volume (TAV), percent atheroma volume (PAV), or plaque volume (PV). Only CRP/hsCRP and LDL-C values reported before and after treatment were considered. Results: 12 studies fulfilled the inclusion criteria and were included in the systematic review. Compared with control groups, meta-analysis of 15 statin-treated arms reported change of TAV/PV showed standardized mean difference (SMD) at -0.27 (95% confidence intervals [CI]: -0.42, -0.12). Meta-analysis of 7 studies reported change of PAV revealed SMD at -0.16 (95% CI: -0.29, -0.03). Meta-regression analysis revealed percent change of CRP/hsCRP statistically influences SMD in change of TAV/PVafter adjusting for percent change of LDL-C, age and gender. Meta-regression analysis showed that percent change of CRP/hsCRP statistically influences SMD in change of PAV. Conclusion: In conclusion, statin therapy is beneficial for plaque regression. Statins promote plaque regression through their anti-inflammatory ability while lowering LDL-C is unaffected. Keywords: Statins; Reduction of atherosclerosis; C-reactive protein; Randomized controlled trial; Meta-analysis

Dark crescent sign: high-risk calcified coronary plaque detected by electrocardiogram-gated non-contrast computed tomography

Electrocardiogram-gated non-contrast computed tomography can discriminate a dark crescent-shaped calcified plaque characterised as a low-intensity area surrounded by high-intensity signals. Careful attention should be paid to performing a percutaneous coronary intervention for a plaque with the dark crescent sign because of its potential high risk of no-flow phenomenon.

398 Right ventricular cardiogenic shock induced by propafenone intoxication: a case report

Aims Propafenone is a Class 1C antiarrhythmic drug recommended in the treatment of supraventricular or ventricular tachycardia and paroxysmal atrial fibrillation (AF). Most common cardiological features associated with propafenone intoxication include heart failure and conduction disturbances while other clinical findings range from nausea and vomiting to seizures and coma. Methods We report a case of atypical presentation of propafenone intoxication occurred in 88-year-old woman who presented at Emergency Department with severe ECG abnormalities and prevalent acute right ventricular with massive tricuspidalic regurgitation and cardiogenic shock. The patient underwent urgent coronary angiography that revealed a stable 90% coronary plaque that was treated with a single stent and then brought to Intensive Care Unit where she was successfully treated with inotropic and mechanical circulatory support (intra-aortic balloon pump, IABP). Results The patient progressively achieved hemodynamic stability with complete ECG normalization and biventricular function recovery. Conclusions Our case further expands the vast spectrum of presentations of Class 1c antiarrhythmic drugs overdose. In an emergency setting it is difficult to rule out other causes of cardiogenic shock but propafenone toxicity needs to be suspected in every case of hemodynamic instability in patients in chronical treatment. Patients in chronical treatment with propafenone who have kidney or liver dysfunction might be at higher risk of drug accumulation: in such cases, the real utility of propafenone must be evaluated before therapy initiation.