PROJECT SUMMARY Transthyretin amyloid cardiomyopathy (ATTR-CM) is a life-threatening multi-system disease that is grossly underdiagnosed, particularly in underserved groups. Early diagnosis of ATTR-CM is crucial to delay disease progression, and now possible with newly available, disease-modifying medications that markedly decrease heart failure hospitalizations and mortality. There is an urgent need to understand the factors associated with delayed diagnosis and the clinical sequelae (Aim 1) and to create interventions to identify patients with ATTR- CM and ensure timely treatment (Aim 2). Aim 1 of this project will identify predictors of delayed diagnosis of ATTR-CM and estimate the effect of delayed diagnosis on patient outcomes using National VA and Medicare data. This will determine the sociodemographic, medical, and geographic factors associated with delayed diagnosis and the association between delayed diagnosis rates at the patient and county level with cardiovascular hospitalization and mortality. Aim 2 will retrospectively assess the implementation of an echo-based machine learning algorithm to assist in the diagnosis of ATTR-CM and evaluate its effect on diagnostic disparities. Characteristics of patients (race, sex, age, social risk/vulnerability, homelessness, vulnerability) first identified through a clinical diagnosis, first identified via the machine learning algorithm (without a pre-existing clinical diagnosis of ATTR-CM), and those with a clinical diagnosis of ATTR-CM who are not captured by the machine learning algorithm will be compared to identify the effect of implementation of the machine learning algorithm on diagnostic disparities. This study will advance the field by identifying groups at risk for delayed diagnosis ATTR-CM that can be targeted by health services interventions. Additionally, it will contribute to the nascent literature on evaluating and ensuring equity in artificial intelligence-based healthcare interventions. It will also advance the applicant’s goal of becoming a clinician-investigator in cardiology with a focus on health equity. Through an expert mentorship team and with strong institutional support, they will: 1) gain critical skills to rigorously interrogate large national databases to identify causes of diagnostic inequality, 2) understand how empirically derived machine learning models improve or worsen existing inequalities, and 3) establish expertise in an emerging important cause of heart failure with evidence of significant existing inequity.