Health Enhanced Artery Risk Tracking With Widespread Implementation and Screening Effort in ASCVD (HEARTWISE-ASCVD)

Summary

This multi-site study will test whether an opportunistic AI-based CAC screening and notification intervention can improve cholesterol treatment and lower cholesterol levels in adults. The study uses artificial intelligence to detect calcium buildup in heart arteries (coronary artery calcium or CAC) on chest CT scans that patients have already had for other reasons. The study will focus on adults who either have known atherosclerotic cardiovascular disease (ASCVD) or have significant calcium buildup (a CAC score of 100 or higher), and whose cholesterol is not well controlled.

It will also evaluate how well this approach can be implemented at scale across multiple health systems. The main questions it aims to answer are:

Does notifying patients and their clinicians about incidental CAC increase lipid-lowering therapy(LLT) initiation or intensification?

Does the intervention improve Low-Density Lipoprotein(LDL)-cholesterol control and related lipid testing?

How does the intervention affect downstream care (e.g., clinic visits, cardiology referrals, and cardiac testing)?

Researchers will use an FDA-cleared AI algorithm to quantify CAC on previously performed non-gated chest CT scans and identify eligible participants through the electronic health record. Participants will be randomized to receive CAC notification either right away or after a 6-month delay.

Conditions

• Atherosclerotic Cardiovascular Disease (ASCVD)
• Coronary Artery Calcification

Interventions

OTHER

AI-Detected CAC Notification and Care Facilitation

After randomization to the early notification arm, the study team will send a standardized notification message to the participant's affiliated clinician. The study team will send a message to the participant after a brief delay. Each site will determine the timing between the initial message to the clinician and the participant based on stakeholder feedback. The notification is about the AI-CAC identified on the participant's previous chest CT. It will provide an overview of AI-CAC, a personalized image of AI-CAC, and a recommended risk discussion with their clinician. These clinicians will also be notified of the findings. For participants randomized to early notification who do not undergo LLT initiation, intensification, or LDL testing within 2 months, the clinician and participant will receive a second message at that time. The participants in the delayed notification arm will receive a similar notification 6 months later.

Sponsors & Collaborators

• Amgen

  collaborator INDUSTRY

• Baylor Scott and White Health

  collaborator OTHER

• Kaiser Permanente

  collaborator OTHER

• Vanderbilt University Medical Center

  collaborator OTHER

• Duke University

  collaborator OTHER

• University of California, Los Angeles

  collaborator OTHER

• Stanford University

  lead OTHER

Principal Investigators

• Fatima Rodriguez, MD, MPH · Stanford University

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

74 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-05-31

Primary Completion

2027-09-30

Completion

2028-03-31

Countries

• United States

Study Locations