Artificial Intelligence for Early Detection of Peripheral Artery Disease

NCT06505317 | Not Yet Recruiting DMC

• 2 other identifiers: (AID-PAD)R01AG084343-01
• Study Type: interventional
• Target: 7,800
• Locations: 0 countries
• Sites: N/A

Brief Summary

The goal of this clinical trial is to test an AI-based screening tool that will help to identify patients at high risk of having undiagnosed peripheral artery disease. The primary outcome measure is overall rate of new PAD diagnoses. Secondary outcomes include rate of new secondary prevention measures initiated for PAD, which will include new prescriptions for antiplatelets, PAD-dosed rivaroxaban, statins, smoking cessation counseling or referrals, and/or supervised exercise therapy referrals also aggregated at a clinic and site level.

Conditions

Peripheral Arterial Disease

Keywords

Peripheral Arterial Disease

Outcome Measures

Primary Outcomes (1)

• PAD Diagnosis Rate

  The primary outcome will be counted at a clinic and site level and will include number of new abnormal ABI tests (ABI\< 0.9), and new diagnosis codes, procedures or affirmative text mentions for PAD for patients without a previous diagnosis

  During 13-39 weeks prior to intervention compared to 13-39 weeks during intervention depending on timing of randomization to intervention period.

Secondary Outcomes (1)

• Initiation of secondary prevention measures

  During 13-39 weeks prior to intervention compared to 13-39 weeks during intervention depending on timing of randomization to intervention period.

Study Arms (3)

Clinical Site 1

EXPERIMENTAL

Randomized to start AI-based PAD screening interventionat week 13.

Diagnostic Test: AI-based PAD screening intervention

Clinical Site 2

EXPERIMENTAL

Randomized to start AI-based PAD screening intervention at Week 26.

Diagnostic Test: AI-based PAD screening intervention

Clinical Site 3

EXPERIMENTAL

Randomized to start AI-based PAD screening intervention at Week 39.

Diagnostic Test: AI-based PAD screening intervention

Interventions

AI-based PAD screening intervention DIAGNOSTIC_TEST

Providers will receive alerts for a patient that is flagged by model as being "high risk" for PAD. This will allow the provider to review the alert, check the patient's previous history, develop additional questions to assess the risk of PAD, and initiate orders prior to seeing a patient. Depending on their assessment during the patient visit the provider may choose to order an ABI test (or perform one at bedside) and/or initiate other secondary prevention measures. All patients for which an alert is triggered will be included for secondary analysis.

Clinical Site 1; Clinical Site 2; Clinical Site 3

Eligibility Criteria

• Age: 50 Years - 85 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Aged 50-85 years
• Presenting to an outpatient appointment at UCSDH, SDVA, or SHC
• No previous diagnosis of PAD
• No prior PAD alert triggered for a previous visit

You may not qualify if:

• \<50 years of age or \> 85 years of age
• Prior diagnosis of PAD

Sponsors & Collaborators

• University of California, San Diego: lead
• Stanford University: collaborator
• National Institute on Aging (NIA): collaborator

Related Publications (1)

• Ghanzouri I, Amal S, Ho V, Safarnejad L, Cabot J, Brown-Johnson CG, Leeper N, Asch S, Shah NH, Ross EG. Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Sci Rep. 2022 Aug 3;12(1):13364. doi: 10.1038/s41598-022-17180-5.

  PMID: 35922657 BACKGROUND

MeSH Terms

Conditions

Peripheral Arterial Disease

Condition Hierarchy (Ancestors)

Atherosclerosis; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Cardiovascular Diseases; Peripheral Vascular Diseases

Study Officials

• Elsie Ross, MD, MSc

  UC San Diego

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Kathleen Groh

CONTACT

8585348103; kagroh@health.ucsd.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Physician

Model Details: A stepped wedge cluster randomization design was chosen as a pragmatic way to evaluate the "real world" impact of AI-based PAD screening. The stepped wedge design has been used to evaluate a variety of interventions, including digital health-based studies. This particular design allows for analysis within and between clusters and can reduce the total number of clusters needed to see an effect, helping increase statistical power compared to parallel cluster randomization. A stepped wedge design, like other cluster randomization designs, also helps reduce possible contamination effects. By using institutions as the basis for clustering, we minimize the possibility that physicians increase their PAD diagnosis rates based on knowledge of the screening tool from adjacent clinics rather than direct use.

Study Record Dates

• First Submitted: July 10, 2024
• First Posted: July 17, 2024
• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): June 30, 2028
• Last Updated: July 17, 2024

Record last verified: 2024-07