**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Angela Dispenzieri, Mayo Clinic, Rochester, MN

**Main Body of the Abstract**
Background: Early detection and treatment of cardiac amyloidosis (CA) is critical to improving patient outcomes. We published an algorithm to detect cardiac amyloidosis using an artificial-intelligence (AI) enhanced analysis of the 12-lead ECG. Despite excellent performance characteristics, any predictive model for rare diseases will be fraught with false positives; therefore, additional predictive modeling for CA including blood, echocardiogram are layered onto the AI ECG score, comprising the A3E score.
Goal: To evaluate the use of the AI ECG and A3E scores in routine practice versus standard care in terms of more CA diagnoses and of provider satisfaction.
Methods: We implemented a randomized pragmatic trial in which providers—mostly cardiologists—were randomized to either an intervention arm or an observation arm. Interventional arm participants are notified of positive ECGs and provided with an orderset to use (if appropriate) for additional amyloid tests and provided with other amyloid resources. The observation arm has access to tools, but receives no notifications. Data are collected on all patients.
Results: As of 31Jan2024, 99 providers have been enrolled and randomized, and 12370 distinct patients’ ECGs have been interrogated. By AI-ECG-score-alone, 1961 ECGs have been positive, but by layering other models on this score, only 294 of these were ultimately called positive. The PPV of the A3E score is 34.0% and the NPV is 99.4%.
Conclusion: The trial is ongoing, but is providing insight into the feasibility and utility of incorporating AI models into clinical practice and support the feasibility for a larger study.



**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Angela Dispenzieri, Mayo Clinic, Rochester, MN

**Main Body of the Abstract**
**Background:** Early detection and treatment of cardiac amyloidosis (CA) is critical to improving patient outcomes. We published an algorithm to detect cardiac amyloidosis using an artificial-intelligence (AI) enhanced analysis of the 12-lead ECG. Despite excellent performance characteristics, any predictive model for rare diseases will be fraught with false positives; therefore, additional predictive modeling for CA including blood, echocardiogram are layered onto the AI ECG score, comprising the A3E score.
**Goal:** To evaluate the use of the AI ECG and A3E scores in routine practice versus standard care in terms of more CA diagnoses and of provider satisfaction.
**Methods:** We implemented a randomized pragmatic trial in which providers—mostly cardiologists—were randomized to either an intervention arm or an observation arm. Interventional arm participants are notified of positive ECGs and provided with an orderset to use (if appropriate) for additional amyloid tests and provided with other amyloid resources. The observation arm has access to tools, but receives no notifications. Data are collected on all patients.
**Results:** As of 31Jan2024, 99 providers have been enrolled and randomized, and 12370 distinct patients’ ECGs have been interrogated. By AI-ECG-score-alone, 1961 ECGs have been positive, but by layering other models on this score, only 294 of these were ultimately called positive. The PPV of the A3E score is 34.0% and the NPV is 99.4%.
**Conclusion:** The trial is ongoing, but is providing insight into the feasibility and utility of incorporating AI models into clinical practice and support the feasibility for a larger study.



Improving Detection of AL Amyloidosis (IDEA)—testing AI cardiac algorithms in the real world PA8 (#271)

We invite you to attend the XIX International Symposium on Amyloidosis to be held in Rochester, MN.

This symposium is organized as a hybrid meeting (with the focus on in-person) held from Sunday, May 26 through Thursday, May 30, 2024.

Together with the ISA Board and members of Mayo Clinic, a very attractive scientific hybrid program is being planned. The program will cover a variety of types of systemic amyloidosis: ATTR (hereditary and wild-type), AL (light-chain), AA and less frequent forms. There will be key lectures, specific symposia including those company sponsored, oral presentations and poster sessions of accepted abstracts.

COURSE DESCRIPTION
This program will provide a comprehensive review of state-of-the-art education in systemic amyloidosis: basic science, pathophysiology, diagnosis, current and emerging treatments. Scientific presentations will include competitive oral and poster abstracts. The rapid evolution of scientific discovery and expanding treatment options has created tremendous interest in the field and this meeting will fill and important gap in educating scientists and clinicians.