ACC 2025

Tempus is a technology company leading the adoption of Al to help providers close care gaps for patients who may have undiagnosed or undertreated cardiovascular disease. We are pleased to share our latest scientific and clinical research findings during the ACC.25 Annual Scientific Conference.

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Abstracts

Abstracts

March 29, 2025

Time
9:30am CST

Abstract #
184

Presenter
Pranav Bhargava

Development of machine learning models incorporating clinical, demographic, and echocardiography variables for predicting left ventricular systolic dysfunction in patients with isolated left ventricular dilation

The goal of this study is to create predictive models for the development of left ventricular (LV) systolic dysfunction in patients with isolated LV dilation (ILVD). This preliminary work identifies predictors and demonstrates the ability to predict progression in patients with ILVD.

Time
11:00am CST

Abstract #
140

Presenter
Tiffany Kelley

Implementation of EHR-integrated notifications on patients at risk for sudden cardiac arrest

This study looks at the implementation of electronic health record (EHR) integrated notifications to identify patients at risk for sudden cardiac arrest (SCA) who may benefit from Implantable Cardioverter Defibrillators (ICD). The conclusion is EHR-integrated notifications can help close care gaps for undermanaged patients at risk for SCA.

Time
11:00am CST

Abstract #
127

Presenter
Gaither Horde

Addressing racial disparities in valvular heart disease: The role of echo-driven EHR alerts in improving care access

The focus of this study is on addressing racial disparities in valvular heart disease, specifically severe aortic stenosis (SAS) and severe mitral regurgitation (SMR). The study observed race-based trends in access to appropriate care for patients at a tertiary care center from April 2019 to January 2024.

March 30, 2025

Time
9:00am CST

Abstract #
174

Presenter
Lauren East

Enhancing diagnostic accuracy and treatment appropriateness in cardiac transthyretin amyloidosis through natural language processing: A retrospective analysis

This study looked at the challenges of diagnosing cardiac transthyretin amyloidosis (cardiac ATTR) and how natural language processing (NLP) can help. The study concludes that significant care gaps exist in managing cardiac ATTR and that integrating NLP into clinical workflows can improve diagnostic accuracy and help patients access therapies.

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