**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.**
Eli Muchtar, Mayo Clinic, Rochester, MN

**Main Body of the Abstract**
Background: We developed an artificial intelligence electrocardiogram (AI-ECG) algorithm to predict the presence of CA. PREDICT-AMY is a randomized two-arm intervention trial that randomizes providers to intervention (receipt of alert on positive AI-ECG score and suggested further diagnostic testing) or control (no alert, usual care) arms. The results of these diagnostic tests are incorporated into enhanced AI-based models to increase CA detection predictive value.
Goal: To explore patient and provider-related factors associated with data availability for these enhanced model calculations in the intervention arm of the study.
Methods/Results: 933 patients with positive AI-ECG scores are included. 83% of patients in the intervention arm had data to calculate at least one enhanced model using non-ECG data. Neither sex nor age were associated with a higher probability of having a calculated enhanced model compared to men (Table). In contrast, >1 ECG available and >1 AI-ECG score suggestive of CA in the past 6 months and lower eGFR increased the probability that sufficient additional testing was done to calculate an enhanced model. Next, we compared providers in the intervention arm (n=47) based on whether certain characteristics predicted sufficient testing to generate >=1 enhanced model for their patients. There was no difference in provider age, sex, or practice years. There was a trend for cardiologists to be above the median of model completeness for at least one patient compared to hematologists (P=0.1).
Conclusion: We demonstrate that our advanced models are feasible in the clinic and their use does not exceed usual practice.



**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.**
Eli Muchtar, Mayo Clinic, Rochester, MN

**Main Body of the Abstract**
**Background:** We developed an artificial intelligence electrocardiogram (AI-ECG) algorithm to predict the presence of CA. PREDICT-AMY is a randomized two-arm intervention trial that randomizes providers to intervention (receipt of alert on positive AI-ECG score and suggested further diagnostic testing) or control (no alert, usual care) arms. The results of these diagnostic tests are incorporated into enhanced AI-based models to increase CA detection predictive value.
**Goal:** To explore patient and provider-related factors associated with data availability for these enhanced model calculations in the intervention arm of the study.
**Methods/Results:** 933 patients with positive AI-ECG scores are included. 83% of patients in the intervention arm had data to calculate at least one enhanced model using non-ECG data. Neither sex nor age were associated with a higher probability of having a calculated enhanced model compared to men (Table). In contrast, >1 ECG available and >1 AI-ECG score suggestive of CA in the past 6 months and lower eGFR increased the probability that sufficient additional testing was done to calculate an enhanced model. Next, we compared providers in the intervention arm (n=47) based on whether certain characteristics predicted sufficient testing to generate >=1 enhanced model for their patients. There was no difference in provider age, sex, or practice years. There was a trend for cardiologists to be above the median of model completeness for at least one patient compared to hematologists (P=0.1).
**Conclusion:** We demonstrate that our advanced models are feasible in the clinic and their use does not exceed usual practice.



Exploring Patient- and Provider Characteristics Associated With The Utilization of Artificial-Intelligence-based Models to Detect Cardiac Amyloidosis (CA): A Subset Analysis From the On-going PREDICT-AMY Trial  PA16 (#561)

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.