The objective of this project is to utilize a longitudinal, VA national cardiovascular disease virtual cohort to investigate traditional, and [new] risk factors (RFs), for Heart Failure (HF), Atrial Fibrillation (AF), and stroke among United States Veterans ≥ 65. This cohort will consist of contemporary and comprehensive electronic health record (EHR) data and will include continued curation and validation of complex phenotypes using innovative analytics. [The specific aims of this research in older Veterans are to: 1) Assess determinants of HF risk and prognosis after HF diagnosis in a VA Healthcare facility, 2), Assess determinants of AF risk and prognosis and 3) Assess determinants of Stroke risk and prognosis.] [During the previous funding cycle, we leveraged the VA’s large scale national longitudinal EHR data, linked to additional data sources including the Centers for Medicare and Medicaid Services (CMS) and the National Death Index (NDI) to create a comprehensive cardiovascular database. Data curation for traditional RFs, ASCVD preventive therapies, and several ASCVD outcomes was also completed in the previous funding cycle. Our new proposal will include continued data curation to include novel RFs, such as red cell indices, white blood cell indices, obstructive sleep apnea, frailty, alcohol intake, aortic stenosis, medication use, lifestyle factors, and blood measures [estimated glomerular filtration rate (eGFR), serum albumin]. We will achieve this by employing high-throughput probabilistic phenotyping and multimodal and network data modeling. Outcomes and risk factors will be evaluated over time where appropriate.] Electronic health record (EHR) data will be used to identify a baseline index date for Veterans based on an outpatient VAMC visit at which time the Veteran had blood assayed for traditional lipids. Information from the baseline and follow up visits will be obtained with database programming and will include: age, sex, systolic BP, diabetes status, cholesterol, HDL-C, TG, and CVD preventive medications. Filtering algorithms will be used for laboratory measures across the different hospital laboratories, as has already been undertaken by this research team. Similarly, BMI, glucose and hemoglobin A1c (HbA1c) measurements, and vascular disease ICD-9/10 codes will be added. [Incidence and prognosis of HF, AF, and stroke will assess the role of established and novel risk profiles. Outcomes to be analyzed include myocardial infarction, stroke, cardiovascular mortality, and all-cause mortality. For HF we will also examine HF hospital admissions and HF re-admissions within 30 days of diagnosis. We will compare currently published algorithms for the three conditions where appropriate and compare the results to our own developed regression models that include novel RFs using mediation analyses and reclassification strategies where appropriate. We will also report county- specific mapping of outcomes.]