Min, James K.

Abstract Aims To investigate the impact of statins on plaque progression according to high-risk coronary atherosclerotic plaque (HRP) features and to identify predictive factors for rapid plaque progression in mild coronary artery disease (CAD) using serial coronary computed tomography angiography (CCTA). Methods and results We analyzed mild stenosis (25–49%) CAD, totaling 1432 lesions from 613 patients (mean age, 62.2 years, 63.9% male) and who underwent serial CCTA at a ≥2 year inter-scan interval using the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (NCT02803411) registry. The median inter-scan period was 3.5 ± 1.4 years; plaques were quantitatively assessed for annualized percent atheroma volume (PAV) and compositional plaque volume changes according to HRP features, and the rapid plaque progression was defined by the ≥90th percentile annual PAV. In mild stenotic lesions with ≥2 HRPs, statin therapy showed a 37% reduction in annual PAV (0.97 ± 2.02 vs. 1.55 ± 2.22, P = 0.038) with decreased necrotic core volume and increased dense calcium volume compared to non-statin recipient mild lesions. The key factors for rapid plaque progression were ≥2 HRPs [hazard ratio (HR), 1.89; 95% confidence interval (CI), 1.02–3.49; P = 0.042], current smoking (HR, 1.69; 95% CI 1.09–2.57; P = 0.017), and diabetes (HR, 1.55; 95% CI, 1.07–2.22; P = 0.020). Conclusion In mild CAD, statin treatment reduced plaque progression, particularly in lesions with a higher number of HRP features, which was also a strong predictor of rapid plaque progression. Therefore, aggressive statin therapy might be needed even in mild CAD with higher HRPs. Clinical trial registration ClinicalTrials.gov NCT02803411

Background: Ischemia with no obstructive coronary artery disease (INOCA) is common and has an adverse prognosis. We set out to describe the natural history of symptoms and ischemia in INOCA. Methods: CIAO-ISCHEMIA (Changes in Ischemia and Angina over One Year in ISCHEMIA Trial Screen Failures With INOCA) was an international cohort study conducted from 2014 to 2019 involving angina assessments (Seattle Angina Questionnaire) and stress echocardiograms 1 year apart. This was an ancillary study that included patients with a history of angina who were not randomly assigned in the ISCHEMIA trial. Stress-induced wall motion abnormalities were determined by an echocardiographic core laboratory blinded to symptoms, coronary artery disease status, and test timing. Medical therapy was at the discretion of treating physicians. The primary outcome was the correlation between the changes in the Seattle Angina Questionnaire angina frequency score and changes in echocardiographic ischemia. We also analyzed predictors of 1-year changes in both angina and ischemia, and we compared CIAO participants with ISCHEMIA participants with obstructive coronary artery disease who had stress echocardiography before enrollment, as CIAO participants did. Results: INOCA participants in CIAO were more often female (66% of 208 versus 26% of 865 ISCHEMIA participants with obstructive coronary artery disease, P <0.001), but the magnitude of ischemia was similar (median 4 ischemic segments [interquartile range, 3–5] both groups). Ischemia and angina were not significantly correlated at enrollment in CIAO ( P =0.46) or ISCHEMIA stress echocardiography participants ( P =0.35). At 1 year, the stress echocardiogram was normal in half of CIAO participants, and 23% had moderate or severe ischemia (≥3 ischemic segments). Angina improved in 43% and worsened in 14%. Change in ischemia over 1 year was not significantly correlated with change in angina (ρ=0.029). Conclusions: Improvement in ischemia and angina were common in INOCA but not correlated. Our INOCA cohort had a degree of inducible wall motion abnormalities similar to concurrently enrolled ISCHEMIA participants with obstructive coronary artery disease. Our results highlight the complex nature of INOCA pathophysiology and the multifactorial nature of angina. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02347215.

Abstract Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the ‘black-box’ criticism), its need for extensive adjudicated (‘labelled’) data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.