A5017974764- Cardiovascular Function and Risk Factors
- ECG Monitoring and Analysis
- Heart Failure Treatment and Management
- Healthcare Policy and Management
- Cardiovascular Health and Risk Factors
- Health Systems, Economic Evaluations, Quality of Life
- Cardiac Imaging and Diagnostics
- Cardiac Arrest and Resuscitation
- Diabetes Treatment and Management
- Machine Learning in Healthcare
- Cardiac Valve Diseases and Treatments
- Artificial Intelligence in Healthcare
- Blood Pressure and Hypertension Studies
- Acute Myocardial Infarction Research
- Cardiac Health and Mental Health
- Primary Care and Health Outcomes
- Emergency and Acute Care Studies
- Artificial Intelligence in Healthcare and Education
- Mechanical Circulatory Support Devices
- Cardiac, Anesthesia and Surgical Outcomes
- Economic and Financial Impacts of Cancer
- Atrial Fibrillation Management and Outcomes
- Lipoproteins and Cardiovascular Health
- Cardiac Structural Anomalies and Repair
- Venous Thromboembolism Diagnosis and Management
Yale University
2017-2025
Yale New Haven Hospital
2020-2025
University of Liverpool
2025
Zephyr Software (United States)
2025
Yale New Haven Health System
2017-2024
University of New Haven
2023-2024
Texas A&M University
2024
Saint Luke's Hospital
2016-2024
University of California, Los Angeles
2021-2024
Cardiovascular Research Center
2022-2023
Publicly available data sets hold much potential, but their unique design may require specific analytic approaches.To determine adherence to appropriate research practices for a frequently used large public database, the National Inpatient Sample (NIS) of Agency Healthcare Research and Quality (AHRQ).In this observational study 1082 studies published using NIS from January 2015 through December 2016, representative sample 120 was systematically evaluated required by AHRQ conduct NIS.None.All...
Background: Obesity may contribute to adverse outcomes in coronavirus disease 2019 (COVID-19). However, studies of large, broadly generalizable patient populations are lacking, and the effect body mass index (BMI) on COVID-19 outcomes— particularly younger adults—remains uncertain. Methods: We analyzed data from patients hospitalized with at 88 US hospitals enrolled American Heart Association’s Cardiovascular Disease Registry collection through July 22, 2020. BMI was stratified by World...
This study sought to develop models for predicting mortality and heart failure (HF) hospitalization outpatients with HF preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure an Aldosterone Antagonist) trial.Although risk assessment are available patients reduced fraction, few have assessed risks death HFpEF.The following 5 methods: logistic regression a forward selection variables; lasso regularization variable selection; random forest (RF);...
Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage care services and shared decision-making, novel methods hold promise for using existing data to generate additional insights.To evaluate whether contemporary machine learning facilitate risk by including a larger number variables identifying complex relationships between predictors outcomes.This cohort study used American College Cardiology Chest Pain-MI Registry identify all AMI...
To examine the effect of 2017 American College Cardiology (ACC)/American Heart Association (AHA) hypertension guidelines on prevalence and eligibility for initiation intensification treatment in nationally representative populations from United States China.Observational assessment data.US National Health Nutrition Examination Survey (NHANES) most recent two cycles (2013-14, 2015-16) China Retirement Longitudinal Study (CHARLS) (2011-12).All 45-75 year old adults who would have a diagnosis...
Importance Wearable devices may be able to improve cardiovascular health, but the current adoption of these could skewed in ways that exacerbate disparities. Objective To assess sociodemographic patterns use wearable among adults with or at risk for disease (CVD) US population 2019 2020. Design, Setting, and Participants This population-based cross-sectional study included a nationally representative sample from Health Information National Trends Survey (HINTS). Data were analyzed June 1...
Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and 2-fold premature death. The use ECG signals in screening for LV limited by their availability to clinicians. We developed novel deep learning-based approach that can images the dysfunction.
Abstract Background and Aims Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity mortality but requires skilled examination with Doppler imaging. This study reports the development validation a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without imaging identify severe AS, suitable for point-of-care ultrasonography. Methods results In training set 5257 studies (17 570 videos) 2016 2020...
Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted single-lead ECGs obtained on wearable and portable devices. use 385,601 development standard noise-adapted model. For model, are augmented during training with random...
Aortic stenosis (AS) is a major public health challenge with 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 using single-view long-axis echocardiography without Doppler characterization.
Optimal health care delivery, both now and in the future, requires a continuous loop of knowledge generation, dissemination, uptake on how best to provide care, not just determining what interventions work but also ensure they are provided those who need them. The randomized clinical trial (RCT) is most rigorous instrument determine works care. However, major issues with trials enterprise lack integration delivery compromise medicine's ability serve society.
Abstract Background and Aims Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy predict HF risk. Methods Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), Brazilian Longitudinal Study of Adult (ELSA-Brasil), individuals without baseline were followed for first hospitalization. An AI-ECG...