Significance of diabetes mellitus status in patients undergoing percutaneous left main coronary artery intervention.

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.

The Efficacy of Coronary Sinus Reducer in Patients with Refractory Angina: A Systematic Review and Meta-Analysis.

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.

Markers of Vascular Dysfunction in Hypertension.

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.

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.

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.

Ticagrelor potentiates cardioprotection by remote ischemic preconditioning: the ticagrelor in remote ischemic preconditioning (TRIP) randomized clinical trial.

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.

Semaglutide Concurrently Improves Vascular and Liver Indices in Patients With Type 2 Diabetes and Fatty Liver Disease.

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.

A More Targeted and Selective Use of Implantable Loop Recorders Improves the Effectiveness of Syncope Units: A Single-Center Experience.

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.

Bridge to Life: Current Landscape of Temporary Mechanical Circulatory Support in Heart-Failure-Related Cardiogenic Shock.

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.

Exercise Intolerance Is Associated with Cardiovascular Dysfunction in Long COVID-19 Syndrome.

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.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

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.

Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning.

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.

Endothelin-1 Plasma and Aqueous Humor Levels in Different Types of Glaucoma: A Systematic Review and Meta-Analysis.

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.

The Association of Systemic Endothelial Dysfunction With Diffuse Diabetic Macular Edema.

Our aim was to assess whether systemic endothelial dysfunction, evaluated non-invasively by flow mediated dilation (FMD), is associated with diabetic macular edema (DME) and to determine if it is further impaired in patients with diffuse-DME. Consecutive patients (n = 84) with type-2 diabetes mellitus (T2DM) and diabetic retinopathy were enrolled. DME was not present in 38 (non-DME) and present in 46 patients; 25 with focal and 21 with diffuse-DME. No differences were detected between DME and non-DME groups regarding the clinical and demographic characteristics, except for the age of T2DM initiation (lower in non-DME). FMD values were significantly impaired in DME compared with non-DME patients, even after adjustment for multiple covariates (3.56 ± 1.03 vs 4.57 ± 1.25%, P = .003). Among DME patients, no differences were found concerning the clinical and demographic data, while FMD levels were significantly lower in diffuse-DME patients, compared with the focal-DME ones, regardless of the impact several confounders (2.88 ± 0.65 vs 4.08 ± 0.95%, P = .002). It is noteworthy that FMD values of non-DME and focal-DME patients did not differ significantly (4.52 ± 1.24 vs 4.21 ± 1.06%, P = .307). Moreover, among DME patients, impaired FMD was an independent predictor of diffuse-DME (odds ratio: 0.06, 95% CI 0.01-0.47, P = .007).

Coronary Plaque Erosion: Epidemiology, Diagnosis, and Treatment.

Plaque erosion (PE), a distinct etiology of acute coronary syndromes (ACSs), is often overshadowed by plaque ruptures (PRs). Concerning its epidemiology, PE has garnered increasing recognition, with recent studies revealing its prevalence to be approximately 40% among ACS patients, challenging earlier assumptions based on autopsy data. Notably, PE exhibits distinct epidemiological features, preferentially affecting younger demographics, particularly women, and often manifesting as a non-ST-segment elevation myocardial infarction. There are seasonal variations, with PE events being less common in winter, potentially linked to physiological changes and cholesterol solidification, while peaking in summer, warranting further investigation. Moving to molecular mechanisms, PE presents a unique profile characterized by a lesser degree of inflammation compared to PR, with endothelial shear stress emerging as a plausible molecular mechanism. Neutrophil activation, toll-like receptor-2 pathways, and hyaluronidase 2 expression are among the factors implicated in PE pathophysiology, underscoring its multifactorial nature. Advancements in intravascular imaging diagnostics, particularly optical coherence tomography and near-infrared spectroscopy coupled with intravascular ultrasound, offer unprecedented insights into plaque composition and morphology. Artificial intelligence algorithms show promise in enhancing diagnostic accuracy and streamlining image interpretation, augmenting clinician decision-making. Therapeutically, the management of PE evolves, with studies exploring less invasive approaches such as antithrombotic therapy without stenting, particularly in cases identified early through intravascular imaging. Additionally, the potential role of drug-coated balloons in reducing thrombus burden and minimizing future major adverse cardiovascular events warrants further investigation. Looking ahead, the integration of advanced imaging modalities, biomarkers, and artificial intelligence promises to revolutionize the diagnosis and treatment of coronary PE, ushering in a new era of personalized and precise cardiovascular care.

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

The Impact of the COVID-19 Pandemic on Elite Swimmers and Water Polo Players: Lessons for the Future.

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.

Impairment in Right Ventricular-Pulmonary Arterial Coupling in Overweight and Obesity.

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.

Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review.

Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

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.

Video-Based Kinematic Analysis of Movement Quality in a Phase 3 Clinical Trial of Troriluzole in Adults with Spinocerebellar Ataxia: A Post Hoc Analysis.

INTRODUCTION: Traditional methods for assessing movement quality rely on subjective standardized scales and clinical expertise. This limitation creates challenges for assessing patients with spinocerebellar ataxia (SCA), in whom changes in mobility can be subtle and varied. We hypothesized that a machine learning analytic system might complement traditional clinician-rated measures of gait. Our objective was to use a video-based assessment of gait dispersion to compare the effects of troriluzole with placebo on gait quality in adults with SCA. METHODS: Participants with SCA underwent gait assessment in a phase 3, double-blind, placebo-controlled trial of troriluzole (NCT03701399). Videos were processed through a deep learning pose extraction algorithm, followed by the estimation of a novel gait stability measure, the Pose Dispersion Index, quantifying the frame-by-frame symmetry, balance, and stability during natural and tandem walk tasks. The effects of troriluzole treatment were assessed in mixed linear models, participant-level grouping, and treatment group-by-visit week interaction adjusted for age, sex, baseline modified Functional Scale for the Assessment and Rating of Ataxia (f-SARA), and time since diagnosis. RESULTS: From 218 randomized participants, 67 and 56 participants had interpretable videos of a tandem and natural walk attempt, respectively. At Week 48, individuals assigned to troriluzole exhibited significant (p = 0.010) improvement in tandem walk Pose Dispersion Index versus placebo {adjusted interaction coefficient: 0.584 [95% confidence interval (CI) 0.137 to 1.031]}. A similar, nonsignificant trend was observed in the natural walk assessment [coefficient: 1.198 (95% CI - 1.067 to 3.462)]. Further, lower baseline Pose Dispersion Index during the natural walk was significantly (p = 0.041) associated with a higher risk of subsequent falls [adjusted Poisson coefficient: - 0.356 [95% CI - 0.697 to - 0.014)]. CONCLUSION: Using this novel approach, troriluzole-treated subjects demonstrated improvement in gait as compared to placebo for the tandem walk. Machine learning applied to video-captured gait parameters can complement clinician-reported motor assessment in adults with SCA. The Pose Dispersion Index may enhance assessment in future research. TRIAL REGISTRATION-CLINICALTRIALS. GOV IDENTIFIER: NCT03701399.