A screening strategy to identify ATTR in the large background population of patients with one or more common ATTR manifestations, would be of significant clinical value.~In addition, novel ATTR therapies have been recently made available or are currently in development in late-stage clinical trials. As early diagnosis and treatment is expected to achieve better outcomes, this makes the development and validation of an easily implemented, rapid and electronically-enabled diagnostic algorithm especially important.~A medical and pharmacy claims-based algorithm was developed to potentially identify patients at risk of having ATTR. The goal of this study is to evaluate the ability of the algorithm to identify patients with ATTR by performing diagnostic clinical work up in patients that the algorithm identifies in a large dataset of patients at Yale.~The primary objective of this study is to evaluate the diagnostic performance of the algorithm in identifying patients with ATTR amyloidosis.~The secondary objective of this study is to estimate the clinical benefit of the algorithm, as measured by the added diagnostic value, i.e. the proportion or rate of patients who were previously undiagnosed. The total obtained prevalence will be assessed and informally compared to the referral-based prevalence of ATTR amyloidosis patients at Yale.
The primary objective of this study is to evaluate the diagnostic performance of an algorithm in identifying patients with ATTR amyloidosis.