Health Enhanced Artery Risk Tracking With Widespread Implementation and Screening Effort in AtheroSclerotic CardioVascular Disease (HEARTWISE-ASCVD) Study
Trial ID: NCT07355894
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-05
Completion date: 2028-03
Sponsor: Stanford University
Collaborators: Amgen; Baylor Scott and White Health; Kaiser Permanente; Vanderbilt University Medical Center; Duke University; University of California, Los Angeles
Conditions: Atherosclerotic Cardiovascular Disease (ASCVD); Coronary Artery Calcification
Interventions: AI-Detected CAC Notification and Care Facilitation
Locations: University of California, Los Angeles; Kaiser Permanente Northern California; Stanford University; Duke University Medical Center; Vanderbilt University Medical Center; Baylor Scott & White
Countries: United States; United States; United States; United States; United States; United States
Enrollment: 120000
Age range: 18 Years - 74 Years
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria:

* Non-gated chest CT performed within the prior 2 years within the health system
* Active health system engagement with an affiliated clinician eligible for notification, defined as ≥1 clinical visit within the prior 2 years AND at least one of the following:

  1. Active enrollment in the site's integrated healthcare plan (e.g., Kaiser Permanente Northern California), OR
  2. LDL-C measured within the health system within the prior 2 years, OR
  3. Outpatient cardiovascular medication prescription within the prior year
* Meets one of the following clinical criteria:

  1. Clinical ASCVD diagnosis (coronary artery disease, peripheral arterial disease, or ischemic cerebrovascular disease) AND AI-detected CAC \>0 on non-gated chest CT, OR
  2. No ASCVD diagnosis AND AI-detected CAC ≥100
* Suboptimal LDL-C control, defined as either: Last LDL-C ≥70 mg/dL in the last 2 years, OR No LDL-C measurement in the last 2 years

Note: Site-level variations and additional refinements may occur based on local stakeholder input and patient population identification.

Exclusion Criteria:

* Dementia
* Heart transplant
* Active hospice
* Other life-limiting illness as determined by the site investigator (e.g., metastatic cancer)
* Additional participant-level exclusions as determined by clinical stakeholders at each site

Outcomes:
Lipid-lowering therapy initiation or intensification (LLTI)