Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)

Xiaoxi Yao; Rozalina G. McCoy; Paul A. Friedman; Nilay D. Shah; Barbara A. Barry; Emma M. Behnken; Jonathan W. Inselman; Zachi I. Attia; Peter A. Noseworthy

doi:10.1016/j.dib.2019.104894

Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)

Xiaoxi Yao, Rozalina G. McCoy, Paul A. Friedman, Nilay D. Shah, Barbara A. Barry, Emma M. Behnken, Jonathan W. Inselman, Zachi I. Attia, Peter A. Noseworthy

Research output: Contribution to journal › Article › peer-review

Abstract

The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups. Such data can be reused and adapted to translate other artificial intelligence algorithms. This article also includes data collection forms we developed for the clinical trial aiming to evaluate the artificial intelligence algorithm. Such materials can be adapted for other clinical trials.

Original language	English (US)
Article number	104894
Journal	Data in Brief
Volume	28
DOIs	https://doi.org/10.1016/j.dib.2019.104894
State	Published - Feb 2020

Keywords

Artificial intelligence
Clinical trial
Electrocardiogram
Heart failure

ASJC Scopus subject areas

General

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10.1016/j.dib.2019.104894

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title = "Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)",

abstract = "The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups. Such data can be reused and adapted to translate other artificial intelligence algorithms. This article also includes data collection forms we developed for the clinical trial aiming to evaluate the artificial intelligence algorithm. Such materials can be adapted for other clinical trials.",

keywords = "Artificial intelligence, Clinical trial, Electrocardiogram, Heart failure",

author = "Xiaoxi Yao and McCoy, {Rozalina G.} and Friedman, {Paul A.} and Shah, {Nilay D.} and Barry, {Barbara A.} and Behnken, {Emma M.} and Inselman, {Jonathan W.} and Attia, {Zachi I.} and Noseworthy, {Peter A.}",

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month = feb,

doi = "10.1016/j.dib.2019.104894",

language = "English (US)",

volume = "28",

journal = "Data in Brief",

issn = "2352-3409",

publisher = "Elsevier BV",

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TY - JOUR

T1 - Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)

AU - Yao, Xiaoxi

AU - McCoy, Rozalina G.

AU - Friedman, Paul A.

AU - Shah, Nilay D.

AU - Barry, Barbara A.

AU - Behnken, Emma M.

AU - Inselman, Jonathan W.

AU - Attia, Zachi I.

AU - Noseworthy, Peter A.

PY - 2020/2

Y1 - 2020/2

N2 - The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups. Such data can be reused and adapted to translate other artificial intelligence algorithms. This article also includes data collection forms we developed for the clinical trial aiming to evaluate the artificial intelligence algorithm. Such materials can be adapted for other clinical trials.

AB - The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups. Such data can be reused and adapted to translate other artificial intelligence algorithms. This article also includes data collection forms we developed for the clinical trial aiming to evaluate the artificial intelligence algorithm. Such materials can be adapted for other clinical trials.

KW - Artificial intelligence

KW - Clinical trial

KW - Electrocardiogram

KW - Heart failure

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Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)

Abstract

Keywords

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