Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis

Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on medical imaging data can provide a strong initialization for downstream supervised classification and segmentation. Given the difficulty of obtaining expert labels for medical image recognition tasks, such an "in-domain" SSL initialization is often desirable due to its improved label efficiency over standard transfer learning. However, most efforts toward SSL of medical imaging data are not adapted to video-based medical imaging modalities. With this progress in mind, we developed a self-supervised contrastive learning approach, EchoCLR, catered to echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR leverages (i) distinct videos of the same patient as positive pairs for contrastive learning and (ii) a frame re-ordering pretext task to enforce temporal coherence. When fine-tuned on small portions of labeled data (as few as 51 exams), EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) over other transfer learning and SSL approaches across internal and external test sets. For example, when fine-tuning on 10% of available training data (519 studies), an EchoCLR-pretrained model achieved 0.72 AUROC (95% CI: [0.69, 0.75]) on LVH classification, compared to 0.61 AUROC (95% CI: [0.57, 0.64]) with a standard transfer learning approach. Similarly, using 1% of available training data (53 studies), EchoCLR pretraining achieved 0.82 AUROC (95% CI: [0.79, 0.84]) on severe AS classification, compared to 0.61 AUROC (95% CI: [0.58, 0.65]) with transfer learning. EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets

Development of a risk score for early saphenous vein graft failure: An individual patient data meta-analysis.

OBJECTIVES: Early saphenous vein graft (SVG) occlusion is typically attributed to technical factors. We aimed at exploring clinical, anatomical, and operative factors associated with the risk of early SVG occlusion (within 12 months postsurgery). METHODS: Published literature in MEDLINE was searched for studies reporting the incidence of early SVG occlusion. Individual patient data (IPD) on early SVG occlusion were used from the SAFINOUS-CABG Consortium. A derivation (n = 1492 patients) and validation (n = 372 patients) cohort were used for model training (with 10-fold cross-validation) and external validation respectively. RESULTS: In aggregate data meta-analysis (48 studies, 41,530 SVGs) the pooled estimate for early SVG occlusion was 11%. The developed IPD model for early SVG occlusion, which included clinical, anatomical, and operative characteristics (age, sex, dyslipidemia, diabetes mellitus, smoking, serum creatinine, endoscopic vein harvesting, use of complex grafts, grafted target vessel, and number of SVGs), had good performance in the derivation (c-index = 0.744; 95% confidence interval [CI], 0.701-0.774) and validation cohort (c-index = 0.734; 95% CI, 0.659-0.809). Based on this model. we constructed a simplified 12-variable risk score system (SAFINOUS score) with good performance for early SVG occlusion (c-index = 0.700, 95% CI, 0.684-0.716). CONCLUSIONS: From a large international IPD collaboration, we developed a novel risk score to assess the individualized risk for early SVG occlusion. The SAFINOUS risk score could be used to identify patients that are more likely to benefit from aggressive treatment strategies

The prognostic role of galectin-3 and endothelial function in patients with heart failure

Background: Heart failure (HF) is nowadays classified as HF with reduced ejection fraction (HFrEF), HF with mildly reduced EF (HFmrEF), and HF with preserved EF (HFpEF). Endothelial dysfunction (assessed by flow-mediated dilatation [FMD]), increased arterial stiffness (assessed by carotid-femoral pulse-wave velocity [PWV]), and galectin-3, a biomarker of myocardial fibrosis, have been linked to major adverse cardiovascular events (MACE) in patients with ischemic HF. Methods: In this study we prospectively enrolled 340 patients with stable ischemic HF. We assessed the brachial artery FMD, carotid-femoral PWV, and galectin-3 levels, and patients were followed up for MACE according to EF group. Results: Interestingly, the FMD values exhibited a stepwise improvement according to left ventricular ejection fraction (LVEF) (HFrEF: 4.74 ± 2.35% vs. HFmrEF: 4.97 ± 2.81% vs. HFpEF: 5.94 ± 3.46%, p = 0.01), which remained significant after the evaluation of possible confounders including age, sex, cardiovascular risk factors, and number of significantly stenosed epicardial coronary arteries (b coefficient: 0.990, 95% confidence interval: 0.166–1.814, p = 0.019). Single-vessel coronary artery disease (CAD) was more frequent in the group of HFpEF (HFrEF: 56% vs. HFmrEF: 64% vs. HFpEF: 73%, p = 0.049). PWV did not display any association with LVEF. Patients who presented MACE exhibited worse FMD values (4.51 ± 2.35% vs. 5.32 ± 2.67%, p = 0.02), and the highest tertile of galectin-3 was linked to more MACEs (36% vs. 5.9%, p = 0.01). Conclusions: Flow-mediated dilatation displayed a linear improvement with LVEF in patients with ischemic HF. Deteriorated values are associated with MACE. Higher levels of galectin-3 might be used for risk stratification of patients with ischemic HF

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Background: Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction. Methods and results: We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62–0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P  Conclusion: The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art. </p

Adipose tissue-derived WNT5A regulates vascular redox signaling in obesity via USP17//RAC1-mediated activation of NADPH oxidases

Obesity is associated with changes in the secretome of adipose tissue (AT), which affects the vasculature through endocrine and paracrine mechanisms. Wingless-related integration site 5A (WNT5A) and secreted frizzled-related protein 5 (SFRP5), adipokines that regulate noncanonical Wnt signaling, are dysregulated in obesity. We hypothesized that WNT5A released from AT exerts endocrine and paracrine effects on the arterial wall through noncanonical RAC1-mediated Wnt signaling. In a cohort of 1004 humans with atherosclerosis, obesity was associated with increased WNT5A bioavailability in the circulation and the AT, higher expression of WNT5A receptors Frizzled 2 and Frizzled 5 in the human arterial wall, and increased vascular oxidative stress due to activation of NADPH oxidases. Plasma concentration of WNT5A was elevated in patients with coronary artery disease compared to matched controls and was independently associated with calcified coronary plaque progression. We further demonstrated that WNT5A induces arterial oxidative stress and redox-sensitive migration of vascular smooth muscle cells via Frizzled 2–mediated activation of a previously uncharacterized pathway involving the deubiquitinating enzyme ubiquitin-specific protease 17 (USP17) and the GTPase RAC1. Our study identifies WNT5A and its downstream vascular signaling as a link between obesity and vascular disease pathogenesis, with translational implications in humans

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Machine learning in precision diabetes care and cardiovascular risk prediction

Abstract Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes

Inflammatory cytokines in atherosclerosis: current therapeutic approaches

The notion of atherosclerosis as a chronic inflammatory disease has intensified research on the role of cytokines and the way these molecules act and interact to initiate and sustain inflammation in the microenvironment of an atherosclerotic plaque. Cytokines are expressed by all types of cells involved in the pathogenesis of atherosclerosis, act on a variety of targets exerting multiple effects, and are largely responsible for the crosstalk among endothelial, smooth muscle cells, leucocytes, and other vascular residing cells. It is now understood that widely used drugs such as statins, aspirin, methotrexate, and colchicine act in an immunomodulatory way that may beneficially affect atherogenesis and/ or cardiovascular disease progression. Moreover, advancement in pharmaceutical design has enabled the production of highly specific antibodies against key molecules involved in the perpetuation of the inflammatory cascade, raising hope for advances in the treatment of atherosclerosis. This review describes the actions and effects of these agents, their potential clinical significance, and future prospects

Prognostic implications of epicardial fat volume quantification in acute pericarditis

Background The pathophysiology of acute pericarditis remains largely unknown, and biomarkers are needed to identify patients susceptible to complications. As adipose tissue has a pivotal role in cardiovascular disease pathogenesis, we hypothesized that quantification of epicardial fat volume (EFV) provides prognostic information in patients with acute pericarditis. Materials and methods Fifty (n = 50) patients with first diagnosis of acute pericarditis were enrolled in this study. Patients underwent a cardiac computerized tomography (CT) scan to quantify EFV on a dedicated workstation. Patients were followed up in hospital for atrial fibrillation (AF) development and up to 18 months for the composite clinical endpoint of development of constrictive, recurrent or incessant pericarditis or poor response to nonsteroidal anti-inflammatory drugs. Results Patients presenting with chest pain had lower EFV vs. patients without chest pain (167.2 +/- 21.7 vs. 105.1 +/- 11.1 cm 3, respectively, P &lt; 0.01); EFV (but not body mass index) was strongly positively correlated with pericardial effusion size (r = 0.395, P = 0.007) and associated with in-hospital AF. At follow-up, patients that reached the composite clinical endpoint had lower EFV (P &lt; 0.05). After adjustment for age, EFV was associated with lower odds ratio for the composite clinical endpoint point of poor response to NSAIDs or the development of constrictive, recurrent or incessant pericarditis during follow-up (per 20 cm 3 increase in EFV: OR = 0.802 [0.656-0.981], P &lt; 0.05). Conclusions We report for the first time a significant association of EFV with the clinical features and the outcome of patients with acute pericarditis. Measurement of EFV by CT may have important prognostic implications in these patients