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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

Burden of cardiovascular disease attributed to air pollution: a systematic review.

BACKGROUND: Cardiovascular diseases (CVDs) are estimated to be the leading cause of global death. Air pollution is the biggest environmental threat to public health worldwide. It is considered a potentially modifiable environmental risk factor for CVDs because it can be prevented by adopting the right national and international policies. The present study was conducted to synthesize the results of existing studies on the burden of CVDs attributed to air pollution, namely prevalence, hospitalization, disability, mortality, and cost characteristics. METHODS: A systematic search was performed in the Scopus, PubMed, and Web of Science databases to identify studies, without time limitations, up to June 13, 2023. Exclusion criteria included prenatal exposure, exposure to indoor air pollution, review studies, conferences, books, letters to editors, and animal and laboratory studies. The quality of the articles was evaluated based on the Agency for Healthcare Research and Quality Assessment Form, the Newcastle-Ottawa Scale, and Drummond Criteria using a self-established scale. The articles that achieved categories A and B were included in the study. RESULTS: Of the 566 studies obtained, based on the inclusion/exclusion criteria, 92 studies were defined as eligible in the present systematic review. The results of these investigations supported that chronic exposure to various concentrations of air pollutants, increased the prevalence, hospitalization, disability, mortality, and costs of CVDs attributed to air pollution, even at relatively low levels. According to the results, the main pollutant investigated closely associated with hypertension was PM2.5. Furthermore, the global DALY related to stroke during 2016-2019 has increased by 1.8 times and hospitalization related to CVDs in 2023 has increased by 8.5 times compared to 2014. CONCLUSION: Ambient air pollution is an underestimated but significant and modifiable contributor to CVDs burden and public health costs. This should not only be considered an environmental problem but also as an important risk factor for a significant increase in CVD cases and mortality. The findings of the systematic review highlighted the opportunity to apply more preventive measures in the public health sector to reduce the footprint of CVDs in human society.

Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study.

Aims: An algorithmic strategy for anatomical vs. functional testing in suspected coronary artery disease (CAD) (Anatomical vs. Stress teSting decIsion Support Tool; ASSIST) is associated with better outcomes than random selection. However, in the real world, this decision is rarely random. We explored the agreement between a provider-driven vs. simulated algorithmic approach to cardiac testing and its association with outcomes across multinational cohorts. Methods and results: In two cohorts of functional vs. anatomical testing in a US hospital health system [Yale; 2013-2023; n = 130 196 (97.0%) vs. n = 4020 (3.0%), respectively], and the UK Biobank [n = 3320 (85.1%) vs. n = 581 (14.9%), respectively], we examined outcomes stratified by agreement between the real-world and ASSIST-recommended strategies. Younger age, female sex, Black race, and diabetes history were independently associated with lower odds of ASSIST-aligned testing. Over a median of 4.9 (interquartile range [IQR]: 2.4-7.1) and 5.4 (IQR: 2.6-8.8) years, referral to the ASSIST-recommended strategy was associated with a lower risk of acute myocardial infarction or death (hazard ratioadjusted: 0.81, 95% confidence interval [CI] 0.77-0.85, P < 0.001 and 0.74 [95% CI 0.60-0.90], P = 0.003, respectively), an effect that remained significant across years, test types, and risk profiles. In post hoc analyses of anatomical-first testing in the Prospective Multicentre Imaging Study for Evaluation of Chest Pain (PROMISE) trial, alignment with ASSIST was independently associated with a 17% and 30% higher risk of detecting CAD in any vessel or the left main artery/proximal left anterior descending coronary artery, respectively. Conclusion: In cohorts where historical practices largely favour functional testing, alignment with an algorithmic approach to cardiac testing defined by ASSIST was associated with a lower risk of adverse outcomes. This highlights the potential utility of a data-driven approach in the diagnostic management of CAD.

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.

Young and older patients with acute myocardial infarction: differences in risk factors and angiographic characteristics.

OBJECTIVE: Although coronary artery disease mainly affects older individuals, the incidence of myocardial infarction (MI) among younger adults (<55 years) has increased during the past decade. Young and older MI patients have different underlying pathophysiologic characteristics, atherosclerotic plaque morphology, and risk factor profiles. METHODS: We studied 977 patients (≤55 years old: 322, >55 years old: 655) who were hospitalized for MI in the previous 5 years. Patients' baseline characteristics and daily habits were recorded. Angiographic characteristics and vascular lesions were detected, and further examinations, including flow-mediated dilation (FMD), pulse wave velocity (PWV), and central augmentation index (AIx), were performed. Biomarkers of inflammation (Interleukin-6, Tumor-Necrosis factor-a, Intercellular Adhesion Molecule 1, and Osteopontin) were also tested. RESULTS: The median age in the younger age group was 49 years [interquartile range (IQR: 44-53)] and 66 years (IQR: 61-73) in the older age group. Arterial hypertension was less prevalent in the young compared to the elderly with MI (47.4% vs. 76.2%, p < 0.01). The younger counterparts presented significantly lower rates of diabetes mellitus (19.3% vs. 30.6%, p < 0.01), dyslipidemia (59% vs. 70.8%, p < 0.01), and atrial fibrillation (2.6% vs. 9.7%, p < 0.01) and were more casual smokers (49.3% vs. 23.8%, p < 0.01) compared to older patients with MI. In terms of arterial stiffness, lower PWV [7.3 m/s (IQR: 6.5-8.4 m/s) vs. 9 m/s (IQR: 8-10.8 m/s), p < 0.01] and AIx (20.5 ± 10.8 vs. 25.5 ± 7.8, p < 0.01) were recorded in the young compared to the elderly with MI. Concerning angiographic characteristics, younger patients were more likely to have none or single-vessel disease (55.6% vs. 45.8%, p < 0.02), whereas the older participants more frequently had three or more vessel disease (23.5% vs. 13.6% in the young, p < 0.02). Although significant disparities in blood test results were detected during the acute phase, the great majority of young MI patients were undertreated. CONCLUSION: Younger patients with MI are more likely to be smokers with impaired PWV measures, present with non-obstructive or single-vessel disease, and often remain undertreated. A better knowledge of the risk factors as well as the anatomic and pathophysiologic processes in young adults will help enhance MI prevention and treatment options in this patient population.

Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized Inference.

Importance: Randomized clinical trials (RCTs) are the standard for defining an evidence-based approach to managing disease, but their generalizability to real-world patients remains challenging to quantify. Objective: To develop a multidimensional patient variable mapping algorithm to quantify the similarity and representation of electronic health record (EHR) patients corresponding to an RCT and estimate the putative treatment effects in real-world settings based on individual treatment effects observed in an RCT. Design: A retrospective analysis of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial (TOPCAT; 2006-2012) and a multi-hospital patient cohort from the electronic health record (EHR) in the Yale New Haven Hospital System (YNHHS; 2015-2023). Setting: A multicenter international RCT (TOPCAT) and multi-hospital patient cohort (YNHHS). Participants: All TOPCAT participants and patients with heart failure with preserved ejection fraction (HFpEF) and ≥1 hospitalization within YNHHS. Exposures: 63 pre-randomization characteristics measured across the TOPCAT and YNNHS cohorts. Main Outcomes and Measures: Real-world generalizability of the RCT TOPCAT using a multidimensional phenotypic distance metric between TOPCAT and YNHHS cohorts. Estimation of the individualized treatment effect of spironolactone use on all-cause mortality within the YNHHS cohort based on phenotypic distance from the TOPCAT cohort. Results: There were 3,445 patients in TOPCAT and 11,712 HFpEF patients across five hospital sites. Across the 63 TOPCAT variables mapped by clinicians to the EHR, there were larger differences between TOPCAT and each of the 5 EHR sites (median SMD 0.200, IQR 0.037-0.410) than between the 5 EHR sites (median SMD 0.062, IQR 0.010-0.130). The synthesis of these differences across covariates using our multidimensional similarity score also suggested substantial phenotypic dissimilarity between the TOPCAT and EHR cohorts. By phenotypic distance, a majority (55%) of TOPCAT participants were closer to each other than any individual EHR patient. Using a TOPCAT-derived model of individualized treatment benefit from spironolactone, those predicted to derive benefit and receiving spironolactone in the EHR cohorts had substantially better outcomes compared with predicted benefit and not receiving the medication (HR 0.74, 95% CI 0.62-0.89). Conclusions and Relevance: We propose a novel approach to evaluating the real-world representativeness of RCT participants against corresponding patients in the EHR across the full multidimensional spectrum of the represented phenotypes. This enables the evaluation of the implications of RCTs for real-world patients. KEY POINTS: Question: How can we examine the multi-dimensional generalizability of randomized clinical trials (RCT) to real-world patient populations?Findings: We demonstrate a novel phenotypic distance metric comparing an RCT to real-world populations in a large multicenter RCT of heart failure patients and the corresponding patients in multisite electronic health records (EHRs). Across 63 pre-randomization characteristics, pairwise assessments of members of the RCT and EHR cohorts were more discordant from each other than between members of the EHR cohort (median standardized mean difference 0.200 [0.037-0.410] vs 0.062 [0.010-0.130]), with a majority (55%) of RCT participants closer to each other than any individual EHR patient. The approach also enabled the quantification of expected real world outcomes based on effects observed in the RCT.Meaning: A multidimensional phenotypic distance metric quantifies the generalizability of RCTs to a given population while also offering an avenue to examine expected real-world patient outcomes based on treatment effects observed in the RCT.

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).

Introduction: Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). Research Methods and Analysis: This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. Ethics and Dissemination: All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study.

Background: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We developed and tested artificial intelligence (AI) models to automate the detection of underdiagnosed cardiomyopathies from cardiac POCUS. Methods: In a development set of 290,245 transthoracic echocardiographic videos across the Yale-New Haven Health System (YNHHS), we used augmentation approaches and a customized loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network (CNN) that discriminates HCM (hypertrophic cardiomyopathy) and ATTR-CM (transthyretin amyloid cardiomyopathy) from controls without known disease. We evaluated the final model across independent, internal and external, retrospective cohorts of individuals who underwent cardiac POCUS across YNHHS and Mount Sinai Health System (MSHS) emergency departments (EDs) (2011-2024) to prioritize key views and validate the diagnostic and prognostic performance of single-view screening protocols. Findings: We identified 33,127 patients (median age 61 [IQR: 45-75] years, n=17,276 [52·2%] female) at YNHHS and 5,624 (57 [IQR: 39-71] years, n=1,953 [34·7%] female) at MSHS with 78,054 and 13,796 eligible cardiac POCUS videos, respectively. An AI-enabled single-view screening approach successfully discriminated HCM (AUROC of 0·90 [YNHHS] & 0·89 [MSHS]) and ATTR-CM (YNHHS: AUROC of 0·92 [YNHHS] & 0·99 [MSHS]). In YNHHS, 40 (58·0%) HCM and 23 (47·9%) ATTR-CM cases had a positive screen at median of 2·1 [IQR: 0·9-4·5] and 1·9 [IQR: 1·0-3·4] years before clinical diagnosis. Moreover, among 24,448 participants without known cardiomyopathy followed over 2·2 [IQR: 1·1-5·8] years, AI-POCUS probabilities in the highest (vs lowest) quintile for HCM and ATTR-CM conferred a 15% (adj.HR 1·15 [95%CI: 1·02-1·29]) and 39% (adj.HR 1·39 [95%CI: 1·22-1·59]) higher age- and sex-adjusted mortality risk, respectively. Interpretation: We developed and validated an AI framework that enables scalable, opportunistic screening of treatable cardiomyopathies wherever POCUS is used. Funding: National Heart, Lung and Blood Institute, Doris Duke Charitable Foundation, BridgeBio.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.

Background: Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods: Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results: Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions: An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.