An Observational Study Using Artificial Intelligence (AI) Algorithms on Electrocardiography (ECG), Point-of-care Ultrasound (POCUS), and Transthoracic Echocardiophy (TTE) to Estimate the Under-diagnosis of Transthyretin Amyloid Cardiomyopathy (ATTR-CM) Across a Diverse Range of US Health Systems.

Broad inclusion and exclusion criteria across all 3 objectives: Inclusion Criteria: * Age 50-95 * At least one retrievable ECG and/or 2D echo file (DICOM or equivalent video file) from EHR. Exclusion Criteria: * Unavailable key demographics (age, gender, race, ethnicity) * Individuals who have opted out of research studies Objective-specific inclusion and exclusion criteria: Primary Objective: Additional exclusion criteria: * For subgroup analyses: when evaluating the prevalence of probable ATTR-CM status across demographic groups, we will exclude those with missing baseline demographic information (age, sex, race, geographic region). Secondary Objective 1: Additional inclusion criteria: * 'Cases': ATTR-CM diagnosis defined by ICD-10 codes (Table 1) OR abnormal bone scintigraphy testing consistent with ATTR-CM OR treatment with an approved transthyretin stabilizer or other ATTR-CM-specific therapy * 'Controls': any individuals not meeting the case definition. In these participants, we will consider all eligible ECG, POCUS, or TTE studies performed up to 12 months before diagnosis (first date of ICD code appearance, abnormal bone scintigraphy or treatment onset, whichever happened first) and any time after. 'Controls' will be drawn from ECGs, POCUS, or TTE studies performed in individuals not meeting the 'case' criteria above, including individuals who have never undergone dedicating testing or those who underwent e.g., bone scintigraphy, but with negative (or equiv…