PROTEUS: A Prospective RCT Evaluating Use of AI in Stress Echocardiography

Background Use of artificial intelligence (AI) in cardiovascular imaging may potentially augment clinical decision-making in disease management, but no prospective randomized controlled trials have assessed the impact on cardiovascular outcomes. This study evaluates whether AI–augmented decision-making is non-inferior to standard decision-making when selecting participants for invasive coronary angiography following stress echocardiography. Methods PROTEUS was a multicenter, parallel-group randomized controlled trial. We enrolled participants undergoing a stress echocardiogram at 20 centers across the United Kingdom between November 2021 and June 2023. Participants were randomly assigned to standard clinical decision-making (control) or decision-making augmented by AI (intervention). The primary end point was appropriate referral for coronary angiography, with true positives defined as severe coronary disease requiring revascularization in participants referred for invasive angiography and false negatives defined as an acute coronary event within 6 months. Secondary analysis examined intervention versus control in prespecified subgroups where interpretation is known to be more challenging. Results Out of 2341 randomly assigned participants, 2213 (94.53%) completed 6 months’ follow-up. Eighty-five participants were referred for angiography, 61 of whom had significant coronary disease. Of the participants not referred, 41 participants had acute coronary syndrome or died within 6 months. The difference between the area under the receiver operating characteristic curve (AUROC) for the intervention (0.63; 95% confidence interval (CI), 0.43 to 0.83) and control (0.55; 95% CI, 0.33 to 0.80), did not meet the prespecified non-inferiority margin of −0.05 (difference, 0.09; 95% CI, −0.22 to 0. 39). The sensitivity in the intervention (64.2%; 95% CI, 33.3 to 80.0%) and control (55.1%; 95% CI, 43.7 to 84.2%) was similar (difference, 9.1%; 95% CI, −21.8 to 39.6%). Likewise, the specificity in the intervention (98.6%; 95% CI, 98.1 to 99.8%) and control (99.2%; 95% CI, 97.2 to 99.5%) was similar (difference, 0.6%; 95% CI, −2.1 to 0.9%). Subgroup analyses suggest potential benefit of AI–augmentation in low-volume stress echocardiography centers. Conclusions AI–augmented decision-making in stress echocardiography did not meet the non-inferiority end point when evaluated in a large, prospective randomized controlled trial, but may be beneficial in low-volume centers. (Funded by the Accelerated Access Collaborative and others; ClinicalTrials.gov number, NCT05028179; ISRCTN number, ISRCTN15113915.)

Institutions

Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2024-10-24
DOI
https://doi.org/10.48620/85241
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PROTEUS: A Prospective RCT Evaluating Use of AI in Stress Echocardiography

Open Access CRIS of the University of Bern
Artificial Intelligence in Healthcare and Education
article

PROTEUS: A Prospective RCT Evaluating Use of AI in Stress Echocardiography

article en

Abstract

Background Use of artificial intelligence (AI) in cardiovascular imaging may potentially augment clinical decision-making in disease management, but no prospective randomized controlled trials have assessed the impact on cardiovascular outcomes. This study evaluates whether AI–augmented decision-making is non-inferior to standard decision-making when selecting participants for invasive coronary angiography following stress echocardiography. Methods PROTEUS was a multicenter, parallel-group randomized controlled trial. We enrolled participants undergoing a stress echocardiogram at 20 centers across the United Kingdom between November 2021 and June 2023. Participants were randomly assigned to standard clinical decision-making (control) or decision-making augmented by AI (intervention). The primary end point was appropriate referral for coronary angiography, with true positives defined as severe coronary disease requiring revascularization in participants referred for invasive angiography and false negatives defined as an acute coronary event within 6 months. Secondary analysis examined intervention versus control in prespecified subgroups where interpretation is known to be more challenging. Results Out of 2341 randomly assigned participants, 2213 (94.53%) completed 6 months’ follow-up. Eighty-five participants were referred for angiography, 61 of whom had significant coronary disease. Of the participants not referred, 41 participants had acute coronary syndrome or died within 6 months. The difference between the area under the receiver operating characteristic curve (AUROC) for the intervention (0.63; 95% confidence interval (CI), 0.43 to 0.83) and control (0.55; 95% CI, 0.33 to 0.80), did not meet the prespecified non-inferiority margin of −0.05 (difference, 0.09; 95% CI, −0.22 to 0. 39). The sensitivity in the intervention (64.2%; 95% CI, 33.3 to 80.0%) and control (55.1%; 95% CI, 43.7 to 84.2%) was similar (difference, 9.1%; 95% CI, −21.8 to 39.6%). Likewise, the specificity in the intervention (98.6%; 95% CI, 98.1 to 99.8%) and control (99.2%; 95% CI, 97.2 to 99.5%) was similar (difference, 0.6%; 95% CI, −2.1 to 0.9%). Subgroup analyses suggest potential benefit of AI–augmentation in low-volume stress echocardiography centers. Conclusions AI–augmented decision-making in stress echocardiography did not meet the non-inferiority end point when evaluated in a large, prospective randomized controlled trial, but may be beneficial in low-volume centers. (Funded by the Accelerated Access Collaborative and others; ClinicalTrials.gov number, NCT05028179; ISRCTN number, ISRCTN15113915.)

Open Access CRIS of the University of Bern
Institut für Soziale Arbeit (DE)
Peace, Justice and strong institutions
Openalex Percentile: Top 72%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.