No patients consented to enrollment

The primary objective of this study is to evaluate the diagnostic performance of an algorithm in identifying patients with ATTR amyloidosis.

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.

Inclusion Criteria:

• Identified by the ATTR diagnostic algorithm and matched by Yale's list of potential subjects defined as:

  1. subjects within the claims dataset that are predicted to be at risk of having ATTR who are also being managed within YNHHS

  2. patients who need to be contacted and offered additional clinical evaluation to determine whether they have a diagnosis of ATTR (non-hereditary or Hereditary ATTR amyloidosis).

     Exclusion Criteria:

• Patients who have opted out of research in the Epic system will be excluded entirely from the study

• Patients who are pregnant or who may become pregnant