Artificial Intelligence for Early Detection of Peripheral Artery Disease

Обзор

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.

Подробное описание

After providers consent to participate in this study, a screening tool will be deployed for their weekly clinics to identify patients at high risk of having undiagnosed PAD. These high risk alerts will be provided after a patient has checked in for their outpatient appointment. The alert will be sent to their treating provider once the visit is initiated in the electronic health record system (EHR). 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. For secondary analysis we will specifically evaluate patients who generated an alert and assess how patient demographics and/or clinical factors are associated with likelihood of ABI testing, rate of abnormal ABIs (i.e. true positive rate), and subsequent initiation of secondary prevention measures.

UC San Diego Health (UCSDH), VA San Diego Health Care (VASDHC), and Stanford Health Care (SHC) will be the sites for study enrollment. UCSDH - La Jolla campus, UCSDH - Hillcrest campus, and VASDHC will begin a pre-intervention observation period at the same time, and then each site will be randomized to begin screening tool intervention in a stepped wedge pattern at 13-week intervals for a total of 52 weeks. We will enroll 10 clinics per site based on power calculations for number of patients needed to screen each week and to minimize the number of alerts per clinic/ provider. After this 52 week period, the Stanford site will serve as a validation site and will undergo randomization of 10 clinical sites to three 13 week intervals for a total of 52 weeks.

Вмешательства

• Диагностический тест AI-based PAD screening intervention
  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 ale

Первичные конечные точки

• PAD Diagnosis Rate [Срок оценки: During 13-39 weeks prior to intervention compared to 13-39 weeks during intervention depending on timing of randomization to intervention period.]

Вторичные конечные точки (1)

• Initiation of secondary prevention measures [Срок оценки: During 13-39 weeks prior to intervention compared to 13-39 weeks during intervention depending on timing of randomization to intervention period.]

Критерии участия

Критерии включения

• 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

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Перекрёстный дизайн

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Список центров уточняется — проверьте первичный протокол.

Публикации

• 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

Идентификаторы

NCT: NCT06505317 · (AID-PAD) · R01AG084343-01

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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