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Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to electrocardiographic (ECG) images as a surrogate for imaging risk biomarkers, and its association with early CTRCD. Methods: Across a U.S.-based health system (2013-2023), we identified 1,550 patients (age 60 [IQR:51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines and/or trastuzumab for breast cancer or non-Hodgkin lymphoma and had ECG performed ≤12 months before treatment. We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to baseline ECG images and defined low, intermediate, and high-risk groups based on AI-ECG LVSD probabilities of <0.01, 0.01 to 0.1, and ≥0.1 (positive screen), respectively. We explored the association with early CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), or LVEF<40%, up to 12 months post-treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain (GLS) and AI-ECG LVSD probabilities in studies performed within 15 days of each other. Results: Among 1,550 patients without known cardiomyopathy (median follow-up: 14.1 [IQR:13.4-17.1] months), 83 (5.4%), 562 (36.3%) and 905 (58.4%) were classified as high, intermediate, and low risk by baseline AI-ECG. A high- vs low-risk AI-ECG screen (≥0.1 vs <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adj.HR 3.35 [95%CI:2.25-4.99]) and LVEF<40% (adj.HR 13.52 [95%CI:5.06-36.10]), respectively. Post-hoc analyses supported longitudinal increases in AI-ECG probabilities within 6-to-12 months of a CTRCD event. Among 1,428 temporally-linked echocardiograms and ECGs, AI-ECG LVSD probabilities were associated with worse GLS (GLS -19% [IQR:-21 to -17%] for probabilities <0.1, to -15% [IQR:-15 to -9%] for ≥0.5 [p<0.001]). Conclusions: AI applied to baseline ECG images can stratify the risk of early CTRCD associated with anthracycline or trastuzumab exposure in the setting of breast cancer or non-Hodgkin lymphoma therapy.

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Background and Aims: Diagnosing transthyretin amyloid cardiomyopathy (ATTR-CM) requires advanced imaging, precluding large-scale testing for pre-clinical disease. We examined the application of artificial intelligence (AI) to echocardiography (TTE) and electrocardiography (ECG) as a scalable strategy to quantify pre-clinical trends in ATTR-CM. Methods: Across age/sex-matched case-control datasets in the Yale-New Haven Health System (YNHHS) we trained deep learning models to identify ATTR-CM-specific signatures on TTE videos and ECG images (area under the curve of 0.93 and 0.91, respectively). We deployed these across all studies of individuals referred for cardiac nuclear amyloid imaging in an independent population at YNHHS and an external population from the Houston Methodist Hospitals (HMH) to define longitudinal trends in AI-defined probabilities for ATTR-CM using age/sex-adjusted linear mixed models, and describe discrimination metrics during the early pre-clinical stage. Results: Among 984 participants referred for cardiac nuclear amyloid imaging at YNHHS (median age 74 years, 44.3% female) and 806 at HMH (69 years, 34.5% female), 112 (11.4%) and 174 (21.6%) tested positive for ATTR-CM, respectively. Across both cohorts and modalities, AI-defined ATTR-CM probabilities derived from 7,423 TTEs and 32,205 ECGs showed significantly faster progression rates in the years before clinical diagnosis in cases versus controls (p time × group interaction ≤0.004). In the one-to-three-year window before cardiac nuclear amyloid imaging sensitivity/specificity metrics were estimated at 86.2%/44.2% [YNHHS] vs 65.7%/65.5% [HMH] for AI-Echo, and 89.8%/40.6% [YNHHS] vs 88.5%/35.1% [HMH] for AI-ECG. Conclusions: We demonstrate that AI tools for echocardiographic videos and ECG images can enable scalable identification of pre-clinical ATTR-CM, flagging individuals who may benefit from risk-modifying therapies.

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Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

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PURPOSE: Coronavirus disease-2019 (COVID-19) affects the cardiovascular system even after the acute phase of the disease. Cardiopulmonary rehabilitation may improve post-COVID-19 symptoms. This study aims to evaluate the impact of a cardiopulmonary rehabilitation program after acute COVID-19 on arterial stiffness, left ventricular function, and ventriculoarterial coupling (VAC). METHODS: Forty-eight adults were examined 1 (T0) and 3-mo (T1) following recovery from COVID-19 and randomized 1:1 to participate or not in a 3-mo rehabilitation program. Matched subjects were enrolled as a non-COVID-19 group. Arterial stiffness was evaluated by carotid-femoral pulse wave velocity (PWV). Left ventricular (LV) systolic performance was evaluated with global longitudinal strain (GLS). The PWV/LV-GLS ratio was calculated as an index of VAC. High-sensitivity C reactive protein (hs-CRP) was measured. RESULTS: At T0, convalescent patients with COVID-19 had impaired PWV ( P = .001) and reduced VAC ( P = .001) compared to non-COVID-19 subjects. PWV (8.15 ± 1.37 to 6.55 ± 0.98 m/sec, P < .001) and LV-GLS (-19.67 ± 1.98 to -21.3 ± 1.93%, P < .001) improved only in convalescent patients with COVID-19 undergoing rehabilitation. Similarly, VAC was only improved in the rehabilitation group (-0.42 ± 0.11 to -0.31 ± 0.06 m · sec -1  ·% -1 , P < .001). A significant improvement in VO 2max was noted after rehabilitation (15.70 [13.05, 21.45] to 18.30 [13.95, 23.75] ml · kg -1  · min -1 , P = .01). Finally, hs-CRP was improved in both groups with a significantly greater improvement in the rehabilitation group. CONCLUSION: A 3-mo rehabilitation program in convalesced patients with COVID-19 enhances the recovery of arterial stiffness, left ventricular function, and VAC, highlighting the beneficial mechanisms of rehabilitation in this patient population.

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BACKGROUND: Influenza vaccination among athletes is a crucial area in sports medicine. This descriptive, cross-sectional study aims to explore the vaccination practices and intentions regarding influenza vaccines among young athletes. METHODS: A structured, questionnaire-based study was conducted among students from the National School of Sports in Greece. The survey was conducted over the period of April to May 2023. Overall, 138 participants participated in the study. RESULTS: More than half of the participants had received a flu vaccine in the past, but only 12.3% were vaccinated against influenza for 2022-2023. The main reasons seemed to be the lack of time (40.6%) and the idea that influenza does not lead to any serious health threats for the participants (36.2%). The main factor that affected their decision to get the flu vaccine or not was the need for more information regarding influenza vaccination (79%). CONCLUSIONS: The recent study showed low vaccination coverage among people of young age participating in sports activities. The qualitative views of the participants highlighted the significance of the lackof a well-organized information program provided by health professionals and coaches.

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BACKGROUND: Diabetes mellitus (DM) is a modifiable risk factor for patients with coronary artery disease (CAD). Treatment with insulin correlates with advanced disease and has been associated with excess cardiovascular risk, but evidence on outcomes of patients with insulin-treated DM (ITDM) undergoing left main percutaneous coronary intervention (LMPCI) remains scarce. AIMS: The aim of the presented study is to evluate the risk attributable to DM and ITDM in patients undergoing LMPCI. METHODS: We included 869 patients undergoing PCI for unprotected LMCAD. The cohort was divided into three subgroups based on diabetic status: No DM, ITDM, and Non-ITDM. The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE), defined as a composite of death, spontaneous myocardial infarction (MI), or stroke at 1 year. Results were adjusted for clinically relevant baseline characteristics. RESULTS: Amongst participants, 58.7% had no DM, 25.9% non-ITDM, and 15.4% ITDM. Diabetics were younger and more likely to be female. They also exhibited higher body mass index as well as prevalence of comorbidities, including hypertension, anemia, and chronic kidney disease. The number of bifurcation lesions and stents used was similar between groups. At 1 year, when compared to no DM, ITDM (25.4% vs. 10.0%, p < 0.01) but not non-ITDM (10.8% vs. 10.0%, p = 0.94) demonstrated higher MACCE. This finding was driven by increased risk of MI. Mortality was 8.4%, 7.8%, and 17.2% for no DM, Non-ITDM, and ITDM, respectively. Results remained unchanged after adjustment. CONCLUSIONS: In a rather contemporary patient population undergoing PCI for LMCAD, ITDM but not non-ITDM was associated with higher risk of 1-year MACCE, primarily driven by MI.

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The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

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Context: The cardiovascular benefits of semaglutide are established; however, its effects on surrogate vascular markers and liver function are not known. Objective: To investigate the effects of semaglutide on vascular, endothelial, and liver function in patients with type 2 diabetes (T2DM) and nonalcoholic fatty liver disease (NAFLD). Methods: Overall, 75 consecutive subjects with T2DM and NAFLD were enrolled: 50 patients received semaglutide 1 mg (treatment group) and 25 patients received dipeptidyl peptidase 4 inhibitors (control group). All patients underwent a clinical, vascular, and hepatic examination with Fibroscan elastography at 4 and 12 months after inclusion in the study. Results: Treatment with semaglutide resulted in a reduction of Controlled Attenuation Parameter (CAP) score, E fibrosis score, NAFLD fibrosis score, Fibrosis-4 (FIB-4) score and perfused boundary region (PBR) at 4 and at 12 months (P < .05), contrary to controls. Patients treated with semaglutide showed a greater decrease of central systolic blood pressure (SBP) (-6% vs -4%, P = .048 and -11% vs -9%, P = .039), augmentation index (AIx) (-59% vs -52%, P = .041 and -70% vs -57%, P = .022), and pulse wave velocity (PWV) (-6% vs -3.5%, P = .019 and -12% vs -10%, P = .036) at 4 and at 12 months, respectively. In all patients, ΔPWV and ΔPBR were correlated with a corresponding reduction of CAP, E fibrosis, NAFLD fibrosis, and FIB-4 scores. Conclusion: Twelve-month treatment with semaglutide simultaneously improves arterial stiffness, endothelial function, and liver steatosis and fibrosis in patients with T2DM and NAFLD.

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Hypertension is a modifiable cardiovascular risk factor and displays a rapidly growing incidence due to aging and the acquisition of an unhealthy lifestyle. Hypertension is linked to the development of target organ damage in several vascular beds such as coronary arteries, peripheral, cerebral, and renal arteries. Besides, along with the presence of other cardiovascular risk factors, it aggravates vascular dysfunction due to the aging process. The mechanisms of vascular dysfunction in hypertension are complex and involve excessive salt intake and water retention, activation of neurohormonal systems, induction of endothelial dysfunction of large arteries and microcirculation, development of arterial stiffness, and complex interactions with cellular pathways of inflammation, oxidative stress, and thrombosis. The extent of vascular dysfunction in patients with hypertension can be assessed by evaluating endothelial function, measuring arterial stiffness, and testing the levels of circulating biomarkers of oxidative stress, pro-inflammatory cytokines, and thrombosis. Assessing these markers in subjects with and without hypertension could aid in identifying those at risk of vascular damage and improving risk prediction for future cardiovascular events. While several lifestyle and pharmacological therapies have shown promise in addressing vascular dysfunction in hypertension, none of these biomarkers have been established as an independent risk factor or treatment target. Therefore, in this article, we review the literature on the evidence that exists regarding the role of vascular dysfunction in the pathophysiology, diagnosis, progression, and treatment of hypertension, highlighting the lack of conclusive evidence in this field.

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PURPOSE: Syncope remains a common medical problem. Recently, the role of dedicated syncope units and implantable loop recorders has emerged in the investigation of unexplained syncope. This study aims to investigate the possibilities for a more rational and targeted use of various diagnostic tools. METHODS: In this retrospective single-center study, 196 patients with unexplained syncope were included between March 2019 and February 2023. Various diagnostic tools were utilized during the investigation, according to clinical judgement. Patients were retrospectively allocated into Group A (including those who, among other tests, underwent loop recorder insertion) and Group B (including patients investigated without loop recorder implantation). Data were compared with Group C, including patients assessed prior to syncope unit establishment. RESULTS: There was no difference between Group A (n = 133) and Group B (n = 63) in the diagnostic yield (74% vs. 76%, p = 0.22). There were significant differences between Groups A and B regarding age (67.3 ± 16.9 years vs. 48.3 ± 19.1 years, p < 0.001) and cause of syncope (cardiogenic in 69% of Group A, reflex syncope in 77% of Group B, p < 0.001). Electrocardiography-based diagnosis occurred in 55% and 19% of Groups A and B, respectively (p < 0.001). The time to diagnosis was 4.2 ± 2.7 months in Group A and 7.5 ± 5.6 months in Group B (p < 0.001). In Group C, the diagnostic yield was 57.9% and the electrocardiography-based diagnostic yield was 18.3%. CONCLUSIONS: A selective use of loop recorders according to clinical and electrocardiographic characteristics increases the effectiveness of the structured syncope unit approach and further preserves financial resources.

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Acute heart failure (HF) presents a significant mortality burden, necessitating continuous therapeutic advancements. Temporary mechanical circulatory support (MCS) is crucial in managing cardiogenic shock (CS) secondary to acute HF, serving as a bridge to recovery or durable support. Currently, MCS options include the Intra-Aortic Balloon Pump (IABP), TandemHeart (TH), Impella, and Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO), each offering unique benefits and risks tailored to patient-specific factors and clinical scenarios. This review examines the clinical implications of recent advancements in temporary MCS, identifies knowledge gaps, and explores promising avenues for future research and clinical application. Understanding each device's unique attributes is crucial for their efficient implementation in various clinical scenarios, ultimately advancing towards intelligent, personalized support strategies.

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Background/Objectives: Cardiorespiratory complications are commonly reported among patients with long COVID-19 syndrome. However, their effects on exercise capacity remain inconclusive. We investigated the impact of long COVID-19 on exercise tolerance combining cardiopulmonary exercise testing (CPET) with resting echocardiographic data. Methods: Forty-two patients (55 ± 13 years), 149 ± 92 days post-hospital discharge, and ten healthy age-matched participants underwent resting echocardiography and an incremental CPET to the limit of tolerance. Left ventricular global longitudinal strain (LV-GLS) and the left ventricular ejection fraction (LVEF) were calculated to assess left ventricular systolic function. The E/e' ratio was estimated as a surrogate of left ventricular end-diastolic filling pressures. Tricuspid annular systolic velocity (SRV) was used to assess right ventricular systolic performance. Through tricuspid regurgitation velocity and inferior vena cava diameter, end-respiratory variations in systolic pulmonary artery pressure (PASP) were estimated. Peak work rate (WRpeak) and peak oxygen uptake (VO2peak) were measured via a ramp incremental symptom-limited CPET. Results: Compared to healthy participants, patients had a significantly (p < 0.05) lower LVEF (59 ± 4% versus 49 ± 5%) and greater left ventricular end-diastolic diameter (48 ± 2 versus 54 ± 5 cm). In patients, there was a significant association of E/e' with WRpeak (r = -0.325) and VO2peak (r = -0.341). SRV was significantly associated with WRpeak (r = 0.432) and VO2peak (r = 0.556). LV-GLS and PASP were significantly correlated with VO2peak (r = -0.358 and r = -0.345, respectively). Conclusions: In patients with long COVID-19 syndrome, exercise intolerance is associated with left ventricular diastolic performance, left ventricular end-diastolic pressure, PASP and SRV. These findings highlight the interrelationship of exercise intolerance with left and right ventricular performance in long COVID-19 syndrome.

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Background and Objectives: Several studies suggest the complex relationship between Endothelin-1 (ET-1) levels with various types of glaucoma. This systematic review and meta-analysis explore ET-1 levels in plasma and aqueous humor among different types of glaucoma. Materials and Methods: A literature search (PubMed, ScienceDirect, Cochrane Library) was made up to April 2024 (PROSPERO: CRD42023430471). The results were synthesized according to PRISMA Guidelines. Results were presented as standardized mean differences (SMD) with 95% confidence intervals (CI). Results: A total of 2597 subjects (1513 patients with glaucoma vs. 1084 healthy controls) from 23 studies were included in a meta-analysis. Notably, patients with glaucoma reported significantly higher plasma levels of ET-1 compared to controls (SMD: 1.21, 95% CI: 0.59-1.82, p < 0.001). Particularly, plasma ET-1 levels were higher in primary open-angle glaucoma (POAG) (SMD: 0.87, 95% CI: 0.09-1.65, p < 0.05), normal-tension glaucoma (SMD: 0.86, 95% CI: 0.27-1.46, p = 0.05), and angle-closure glaucoma patients (SMD: 1.03, 95% CI: 0.43-1.63, p < 0.001) compared to healthy controls. Moreover, ET-1 aqueous humor levels were significantly higher in patients with glaucoma compared to controls (SMD: 1.60, 95% CI: 1.04-2.15, p < 0.001). In particular, aqueous humor levels were higher in POAG patients (SMD: 2.03 95% CI: 1.00-3.14, p < 0.001), and pseudoexfoliative glaucoma patients (SMD: 2.03, 95% CI: 1.00-3.07, p < 0.001) compared to controls. Conclusions: This meta-analysis indicates that elevated levels of ET-1 plasma and aqueous humor are significantly associated with different types of glaucoma. The pathogenesis of ET-1-related mechanisms may vary across different glaucoma types, indicating that possible therapeutic approaches targeting ET-1 pathways should be tailored to each specific glaucoma type.

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Background: Refractory angina is a frequently encountered phenomenon in patients with coronary artery disease, often presenting therapeutic challenges to the clinical cardiologist. Novel treatment methods have been explored in this direction, with the coronary sinus reducer (CSR) being among the most extensively-investigated. Methods: We conducted a systematic review of the literature for studies assessing the efficacy of CSR in patients with refractory angina. The primary endpoints of interest were procedural success and the improvement in angina according to the Canadian Cardiovascular Society (CCS) by at least one class. Secondary endpoints were the rate of periprocedural adverse events, the improvement by at least 2 CCS classes, and the mean change in CCS class. A random-effects meta-analysis of proportions (procedural success, improvement by ≥ 1 or ≥ 2 classes, periprocedural adverse events) or means (mean CCS class change) were performed. I 2 was chosen as the metric for between-study heterogeneity. Publication bias was assessed by the inspection of funnel plots and Egger's regression test. We examined the risk of bias according to the Newcastle-Ottawa Scale. Results: From a total of 515 studies identified from the original search, 12 studies were finally included for data extraction. Based on their meta-analysis, we observed a high CSR procedural success (98%, 95% confidence interval (CI) 96 to 99%) with a low rate of periprocedural complications (6%, 95% CI 5 to 7%), while most patients exhibited an improvement by at least 1 CCS class (75%, 95% CI 66 to 83%) after the intervention. A significant proportion of patients demonstrated an improvement by at least 2 CCS classes (39%, 95% CI 34 to 45%), with a mean change of -1.24 CCS class (95% CI -1.40 to -1.08). Conclusions: CSR is associated with high implantation success rates and significant improvements in angina symptoms for patients with refractory angina.

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Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.

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OBJECTIVE: Remote ischemic preconditioning (RIPC) reduces periprocedural myocardial injury (PMI) after percutaneous coronary intervention (PCI) through various pathways, including an adenosine-triggered pathway. Ticagrelor inhibits adenosine uptake, thus may potentiate the effects of RIPC. This randomized trial tested the hypothesis that ticagrelor potentiates the effect of RIPC and reduces PMI, assessed by post-procedural troponin release. METHODS: Patients undergoing PCI for non-ST elevation acute coronary syndromes were 1:1 randomized to ticagrelor (TG-Group) or clopidogrel (CL-Group). Within each treatment, patients were 1:1 randomized to a RIPC (RIPC-Group) or a control group (CTRL-Group). The primary endpoint was the difference between post- and pre-procedural troponin at 24 h following PCI, termed deltaTnI. RESULTS: During a 12-month period, 138 patients were included in the study (34 in the CL-CTRL group, 34 in the TG-CTRL group, 35 in the CL-RIPC group, and 35 in the TG-CTRL group). There was a significant difference in deltaTnI between the study groups [ TG-RIPC:0.04 (0-0.16), CL-CTRL:0.10 (0.03-0.43), CLRIPC:0.11 (0.03-0.89), and TG-CTRL:0.24 (0.06-0.47); p = 0.007]. Eight patients (22.9%) in the TG-RIPC group developed type 4a myocardial infarction (MI), compared to 14 (40%) in the CL-RIPC group, 13 (38.2%) in the CL-CTRL group, and 19 (55.9%) in the TG-CTRL group (p = 0.048). A significant interaction between antiplatelet group allocation and RIPC on deltaTnI was observed [F (1,134) = 7.509; p = 0.007]. In multivariate analysis, the interaction between RIPC and ticagrelor treatment was independently associated with a lower incidence of Type 4a MI. CONCLUSION: Our results demonstrate an interaction between ticagrelor and RIPC, which may potentiate the cardioprotective effects of RIPC during PCI by reducing PMI.

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BACKGROUND: Advances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for medical image recognition tasks. However, most efforts toward SSL in medical imaging are not adapted to video-based modalities, such as echocardiography. METHODS: We developed a self-supervised contrastive learning approach, EchoCLR, for echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR pretraining involves (i) contrastive learning, where the model is trained to identify distinct videos of the same patient, and (ii) frame reordering, where the model is trained to predict the correct of video frames after being randomly shuffled. RESULTS: When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improves classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) over other transfer learning and SSL approaches across internal and external test sets. When fine-tuning on 10% of available training data (519 studies), an EchoCLR-pretrained model achieves 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 achieves 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. CONCLUSIONS: EchoCLR is unique in its ability to learn representations of echocardiogram videos and demonstrates that SSL can enable label-efficient disease classification from small amounts of labeled data.

Artificial intelligence (AI) has been used to develop software that can automatically diagnose diseases from medical images. However, these AI models require thousands or millions of examples to properly learn from, which can be very expensive, as diagnosis is often time-consuming and requires clinical expertise. Using a technique called self-supervised learning (SSL), we develop an AI method to effectively diagnose heart disease from as few as 50 instances. Our method, EchoCLR, is designed for echocardiography, a key imaging technique to monitor heart health, and outperforms other methods on disease diagnosis from small amounts of data. This method can advance AI for echocardiography and enable researchers with limited resources to create disease diagnosis models from small medical imaging datasets.

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BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

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Background: The association of obesity with right ventricular function and the interplay between right heart and pulmonary circulation is incompletely understood. We evaluate the role of obesity as a determinant of right ventricular-pulmonary artery coupling (RVAC). Methods: We retrospectively studied consecutive subjects without overt cardiovascular or pulmonary disease. Subjects were stratified according to body mass index (BMI) as normal weight, overweight, or obese. A transthoracic echocardiographic study was used to assess left and right heart functional and structural parameters. RVAC was assessed using the ratio of peak systolic velocity of the tricuspid annulus to pulmonary artery systolic pressure (PASP). Results: A total of 145 subjects were enrolled with diabetes mellitus incidence higher in obese. There was no difference in left ventricular global longitudinal strain and in PASP or markers of right ventricular systolic function based on BMI. RVAC was significantly lower in the presence of obesity (normal weight: 0.52 (0.19) cm·(sec·mmHg)-1 vs. overweight: 0.47 (0.16) cm·(sec·mmHg)-1 vs. obese: 0.43 (0.14) cm·(sec·mmHg)-1, p = 0.03), even after adjustment for confounders (ß: -0.085, 95% confidence interval: -0.163, -0.009, p = 0.029). Conclusions: Our findings highlight the relationship between metabolic impairment and RVAC, suggesting additional mechanisms for heart failure development observed in obese subjects.

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BACKGROUND: The COVID-19 pandemic has disrupted global daily life, including the world of elite athletes. This paper examines the multifaceted impact the COVID-19 pandemic had on elite swimmers and water polo athletes, specifically their mental health, their concerns over the virus, their intentions of getting vaccinated, and sleep disturbances that they may have faced. METHODS: We conducted a cross-sectional study on elite swimmers and water polo players, using an anonymous questionnaire. RESULTS: A total of 200 elite athletes participated. The majority of the participants reported a negative impact on their mental health, screened positive for insomnia (n = 107 (53.5%), with females (n = 101; 57.7%), swimmers (n = 100, 66.7%), and university students (n = 71, 71.7%) being more vulnerable (p < 0.001). Concerns about contracting the disease especially during important training or tournament periods and potential career disruption also affected their psychological well-being. While the majority (75%) had the intention of getting vaccinated, an alarming percentage was yet uncertain over its decision. CONCLUSIONS: This study highlights the significant psychological distress faced by elite aquatic athletes during the pandemic. It emphasizes the difficulties faced by elite swimmers and water polo athletes and determines not only the importance of addressing the vaccination intentions of athletes, but also how critical it is to confront the challenges they face both for their personal health and for the restoration of world sports to their pre-pandemic state. More large-scale studies are required to inform policies targeted at minimizing disruption to the athletes' career, provision of information on preventive measures and vaccination, and improvement in psychological well-being in case of similar major public health issues in the future. Additionally, this study calls for further research to explore the unique challenges faced by aquatic athletes, such as those related to their training environments and fear of contagion, to better support them in future public health crises.