Project Summary/Abstract More than 6 million adults are suffering from heart failure in the United States. Heart failure is associated with high mortality rate while also reducing the quality of life. Early recognition of heart failure and timely interventions can help reducing the disease burden to individuals and to overall healthcare system. However, more than half of HF patients are HF with preserved left ventricular ejection fraction (HFpEF) while the majority of existing HF treatments are for HF with reduced left ventricular ejection fraction (HFrEF). This is because HFpEF is a heterogenous syndrome, and its etiology is not well understood. A new NIH-funded initiative, HeartShare Study, aims to fill this knowledge gap to identify subtypes of HFpEF potentially with different treatment options using deep phenotyping, multi-omics, and machine learning approach. However, there is still a need for low cost and accessible tools 1) for screening large patient populations for HFpEF risk to support preventive risk modification strategies and 2) for identifying HFpEF subtypes to assist targeted therapeutics. The goal of this ancillary study is to utilize low cost and accessible electrocardiogram (ECG) data via artificial intelligence (AI) for prediction of incident HFpEF risk and subtyping of prevalent HFpEF. We and others have shown that AI applied to ECG data can discriminate patients with reduced and preserved EF with high accuracy [1-5]. We recently developed and validated an ECG-based 10-year HF risk prediction model using artificial intelligence (AI) [6, 7]. These findings led us to hypothesize that AI applied to ECG data can predict HFpEF risk and identify specific HFpEF subtypes. The goal of this ancillary study is to test our hypothesis by leveraging retrospective ECG and clinical data from: a) NIH-funded studies with gold standard ascertainment of HFpEFand b) real-world ECG and clinical data from three large healthcare systems (WFU- Wake Forest University, Winston-Salem, NC; UT-University of Tennessee Health Science Center, Memphis, TN; and LUC-Loyola University Chicago) and c) data from the HeartShare Study. Building on our expertise, we propose developing ECG-based risk prediction and classification of HFpEF subtypes by completing three Aims: Aim 1. Develop an incident HFpEF prediction model using data from NIH-funded studies: We will utilize high quality and accurate data from NIH-funded studies to develop AI model predicting risk for incident HFpEF. Aim 2. Develop an incident HFpEF prediction model using real-world Electronic Health Records (EHR)- derived data: We will first utilize very larger and diverse EHR-based real world data to develop incident HFpEF risk prediction model. We will then harmonize it with the NIH-data based model via transfer learning. Aim 3. Develop, test and implement ECG-based HFpEF phenotyping. This aim will utilize data from prevalent HFpEF patients to classify HFpEF subtypes.