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Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial - 09/07/21

Doi : 10.1016/j.ahj.2021.05.006 
Xiaoxi Yao, PhD 1, 2, , Zachi I. Attia, MS 2, Emma M. Behnken 3, Kelli Walvatne, MBA 1, Rachel E. Giblon, MS 4, Sijia Liu, PhD 4, Konstantinos C. Siontis, MD 2, Bernard J. Gersh, MB, ChB, DPhil 2, Jonathan Graff-Radford, MD 5, Alejandro A. Rabinstein, MD 5, Paul A. Friedman, MD 2, Peter A. Noseworthy, MD 2
1 Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 
2 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 
3 Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN 
4 Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 
5 Department of Neurology, Mayo Clinic, Rochester, MN 

Reprint requests: Xiaoxi Yao, PhD, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN 55905.Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic200 First Street SWRochesterMN55905

Highlights

The BEAGLE study will employ digital technologies and other pragmatic features designed to improve the efficiency of clinical trials.
These features include:
``Siteless'' design in which enrollment, intervention, and follow-up are conducted remotely.
Video-based enrollment and study consent processes.
Testing of a novel AI-ECG algorithm as well as automated EHR-based NLP algorithms for the detection of previously undiagnosed AF among patients at risk of stroke.

Il testo completo di questo articolo è disponibile in PDF.

Riassunto

Background

Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.

Objectives

To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF).

Design

We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected. Eligible patients will be identified using digital phenotyping algorithms, including natural language processing that runs on the electronic health records. Study invitations will be sent in batches via patient portal or letter, which will direct patients to a website to verify eligibility, learn about the study (including video-based informed consent), and consent electronically. The method aims to enroll participants representative of the general patient population, rather than a convenience sample of patients presenting to clinic. A device will be mailed to patients to continuously monitor for up to 30 days. The primary outcome is AF diagnosis and burden; secondary outcomes include patients’ experience with the trial conduct methods and the monitoring device. The enrollment, intervention, and follow-up will be conducted remotely, ie, a patient-centered site-less trial.

Summary

This is among the first wave of trials to adopt digital technologies, artificial intelligence, and other pragmatic features to create efficiencies, which will pave the way for future trials in a broad range of disease and treatment areas.

Clinicaltrials.gov: NCT04208971

Il testo completo di questo articolo è disponibile in PDF.

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© 2021  Pubblicato da Elsevier Masson SAS.
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