Stroke-bleeding risk score pairings within individuals in the outcomes registry for better informed treatment of atrial fibrillation (ORBIT-AF) registry.

Background: Decisions about stroke prevention strategies in atrial fibrillation (AF) typically balance thromboembolism reduction against increased bleeding from oral anticoagulation therapy (OAC). When determining eligibility for OAC, guidelines recommend calculation of thromboembolic event rates using a validated score such as CHA2DS2-VASc. In contrast, routine calculation of bleeding scores is not recommended, in part because many patient factors associated with an increased risk of bleeding are associated with an even larger increased risk of ischemic stroke.

Resting Heart Rate and Associations With Clinical Measures From the Project Baseline Health Study: Observational Study.

Background: Though widely used, resting heart rate (RHR), as measured by a wearable device, has not been previously evaluated in a large cohort against a variety of important baseline characteristics.

Objective: This study aimed to assess the validity of the RHR measured by a wearable device compared against the gold standard of ECG (electrocardiography), and assess the relationships between device-measured RHR and a broad range of clinical characteristics.

Methods: The Project Baseline Health Study (PHBS) captured detailed demographic, occupational, social, lifestyle, and clinical data to generate a deeply phenotyped cohort.

Effects of GLP-1 receptor agonists on kidney and cardiovascular disease outcomes: a meta-analysis of randomised controlled trials.

Background: GLP-1 receptor agonists reduce the risk of major adverse cardiovascular events (MACE) and can also have kidney benefits. However, whether GLP-1 receptor agonists improve clinically important kidney outcomes remains uncertain. We aimed to comprehensively assess the effects of GLP-1 receptor agonists on kidney and cardiovascular disease outcomes by performing a meta-analysis of randomised controlled trials.

Leveraging Deep Learning of Chest Radiograph Images to Identify Individuals at High Risk for Chronic Obstructive Pulmonary Disease.

Background: This study assessed whether deep learning applied to routine outpatient chest X-rays (CXRs) can identify individuals at high risk for incident chronic obstructive pulmonary disease (COPD).

Methods: Using cancer screening trial data, we previously developed a convolutional neural network (CXR-Lung-Risk) to predict lung-related mortality from a CXR image. In this study, we externally validated CXR-Lung-Risk to predict incident COPD from routine CXRs.