Role of biomechanical forces in the natural history of coronary atherosclerosis.

Atherosclerosis remains a major cause of morbidity and mortality worldwide, and a thorough understanding of the underlying pathophysiological mechanisms is crucial for the development of new therapeutic strategies. Although atherosclerosis is a systemic inflammatory disease, coronary atherosclerotic plaques are not uniformly distributed in the vascular tree. Experimental and clinical data highlight that biomechanical forces, including wall shear stress (WSS) and plaque structural stress (PSS), have an important role in the natural history of coronary atherosclerosis. Endothelial cell function is heavily influenced by changes in WSS, and longitudinal animal and human studies have shown that coronary regions with low WSS undergo increased plaque growth compared with high WSS regions. Local alterations in WSS might also promote transformation of stable to unstable plaque subtypes. Plaque rupture is determined by the balance between PSS and material strength, with plaque composition having a profound effect on PSS. Prospective clinical studies are required to ascertain whether integrating mechanical parameters with medical imaging can improve our ability to identify patients at highest risk of rapid disease progression or sudden cardiac events.This work was supported by the British Heart Foundation (FS/13/33/30168), Heart Research UK (RG2638/14/16), the Cambridge NIHR Biomedical Research Centre, and the BHF Cambridge Centre for Research Excellence.This is the author accepted manuscript. The final version is available from Nature Publishing Group at http://dx.doi.org/10.1038/nrcardio.2015.203

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Novel Biomarker of Oxidative Stress Is Associated With Risk of Death in Patients With Coronary Artery Disease

BACKGROUND: Free radical scavengers have failed to improve patient outcomes, promoting the concept that clinically important oxidative stress may be mediated by alternative mechanisms. We sought to examine the association of emerging aminothiol markers of nonfree radical mediated oxidative stress with clinical outcomes. METHODS AND RESULTS: Plasma levels of reduced (cysteine and glutathione) and oxidized (cystine and glutathione disulphide) aminothiols were quantified by high performance liquid chromatography in 1411 patients undergoing coronary angiography (mean age 63 years, male 66%). All patients were followed for a mean of 4.7 ± 2.1 years for the primary outcome of all-cause death (n=247). Levels of cystine (oxidized) and glutathione (reduced) were associated with risk of death (P+1 SD and <-1 SD, respectively) were associated with higher mortality (adjusted hazard ratio [HR], 1.63; 95% confidence interval [CI], 1.19-2.21; HR, 2.19; 95% CI, 1.50-3.19; respectively) compared with those outside these thresholds. Furthermore, the ratio of cystine/glutathione was also significantly associated with mortality (adjusted HR, 1.92; 95% CI, 1.39-2.64) and was independent of and additive to high-sensitivity C-reactive protein level. Similar associations were found for other outcomes of cardiovascular death and combined death and myocardial infarction. CONCLUSIONS: A high burden of oxidative stress, quantified by the plasma aminothiols, cystine, glutathione, and their ratio, is associated with mortality in patients with coronary artery disease, a finding that is independent of and additive to the inflammatory burden. Importantly, these data support the emerging role of nonfree radical biology in driving clinically important oxidative stress

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis : From the PARADIGM Registry

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume 651.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Results from the PARADIGM registry

Funding Information: This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (Ministry of Science and ICT; Ministry of Trade, Industry and Energy; Ministry of Health & Welfare, Republic of Korea; and Ministry of Food and Drug Safety; Project Number: 202016B02) and funded in part by a generous gift from the Dalio Institute of Cardiovascular Imaging and the Michael Wolk Foundation. This work was also supported by the National Research Foundation of Korea [RS‐2022‐00165404, 2022R1A5A6000840, 2020R1I1A1A01073151]. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Publisher Copyright: © 2023 The Authors. Clinical Cardiology published by Wiley Periodicals, LLC.Background and Hypothesis: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. Methods: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. Results: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%–50% and 5.6%–7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. Conclusions: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.publishersversionepub_ahead_of_prin

Sex Differences in Instantaneous Wave-Free Ratio or Fractional Flow Reserve–Guided Revascularization Strategy

Objectives: This study sought to evaluate sex differences in procedural characteristics and clinical outcomes of instantaneous wave-free ratio (iFR)– and fractional flow reserve (FFR)–guided revascularization strategies. Background: An iFR-guided strategy has shown a lower revascularization rate than an FFR-guided strategy, without differences in clinical outcomes. Methods: This is a post hoc analysis of the DEFINE-FLAIR (Functional Lesion Assessment of Intermediate stenosis to guide Revascularization) study, in which 601 women and 1,891 men were randomized to iFR- or FFR-guided strategy. The primary endpoint was 1-year major adverse cardiac events (MACE), a composite of all-cause death, nonfatal myocardial infarction, or unplanned revascularization. Results: Among the entire population, women had a lower number of functionally significant lesions per patient (0.31 ± 0.51 vs. 0.43 ± 0.59; p &lt; 0.001) and less frequently underwent revascularization than men (42.1% vs. 53.1%; p &lt; 0.001). There was no difference in mean iFR value according to sex (0.91 ± 0.09 vs. 0.91 ± 0.10; p = 0.442). However, the mean FFR value was lower in men than in women (0.83 ± 0.09 vs. 0.85 ± 0.10; p = 0.001). In men, an FFR-guided strategy was associated with a higher rate of revascularization than an iFR-guided strategy (57.1% vs. 49.3%; p = 0.001), but this difference was not observed in women (41.4% vs. 42.6%; p = 0.757). There was no difference in MACE rates between iFR- and FFR-guided strategies in both women (5.4% vs. 5.6%, adjusted hazard ratio: 1.10; 95% confidence interval: 0.50 to 2.43; p = 0.805) and men (6.6% vs. 7.0%, adjusted hazard ratio: 0.98; 95% confidence interval: 0.66 to 1.46; p = 0.919). Conclusions: An FFR-guided strategy was associated with a higher rate of revascularization than iFR-guided strategy in men, but not in women. However, iFR- and FFR-guided strategies showed comparable clinical outcomes, regardless of sex. (Functional Lesion Assessment of Intermediate Stenosis to guide Revascularization [DEFINE-FLAIR]; NCT02053038