**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.**
Ruizhi Liao, Empallo Inc., Boston, MA, ray@empallo.com

**Main Body of the Abstract**
Accurate tools are needed to better phenotype and risk stratify transthyretin amyloid cardiomyopathy (ATTR-CM) patients. We developed an AI model to predict mortality among ATTR-CM patients and identify clinical features that indicate a poor prognosis. Our study utilized expert-certified de-identified EHR data of 1,727 ATTR-CM patients. Clinical features including demographics, comorbidities, laboratories, and echocardiographic variables were included in a deep neural network programmed to predict all-cause mortality. A randomly selected subset of 1,382 (80%) patients was used to train the AI model to predict 2- and 5-year mortality. This model was then tested on a holdout set (n=345, 20%). The AUC was ascertained. Sensitivity analyses were used to rank clinical features most predictive of mortality. Of the 1,727 pts, 259 and 504 patients were deceased at 2- and 5-years, respectively. The AI model had an AUC of 0.87 (95% CI, 0.81-0.91) and 0.83 (95% CI, 0.78-0.88) for predicting 2- and 5-year mortality, respectively. The 5 most predictive clinical features were NT-proBNP, age, chronic pulmonary disease, cardiac index, and LV stroke volume index (SVI) in the 2-year mortality model, compared to NT-proBNP, chronic pulmonary disease, creatinine, age, and LV SVI in the 5-year model. Utilizing EHR-derived clinical features, AI models are able to accurately predict mortality of ATTR-CM patients and identify clinical features that indicate poor prognosis. Future applications could be used to predict which patient risk profile, if any, responds better to ATTR-CM directed therapies, and deserve further study.



Machine Learning to Predict Mortality among Patients with Transthyretin Amyloid Cardiomyopathy PA14 (#545)

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.