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.

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Journal
NEJM AI
Published
2024-10-18
DOI
https://doi.org/10.1056/aioa2400865
Citations
17
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
6.87
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article

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

Ben Thompson, Emily Sanderson, Tine Descamps, Mamta Bajre et al.
17 citations
NEJM AI
Cardiac Imaging and Diagnostics
6.87
article

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

Ben Thompson, Emily Sanderson, Tine Descamps, Mamta Bajre, Helen Dawes, William Hawkes, William Woodward, Rizwan Sarwar, Victoria Williamson, Jamie M. O’Driscoll, Ross Upton, Sanjeev Bhattacharyya, Casey L Johnson, Thomas H. Marwick, Maria Breen, Gerard R. Ridgway, Steffen Erhard Petersen, Paul Leeson, Hakim‐Moulay Dehbi, Eleni Frangou, Virginia Chiocchia, Ashley P. Akerman, David P Ripley, Natalie Savage, Victoria Harris, Samuel Krasner, Rajan Sharma, Hania Piotrowska, Gary Woodward
article en
17 citations

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.

NEJM AIVol. 1(11)
University of Bern (CH), St Bartholomew's Hospital (GB), Baker Heart and Diabetes Institute (AU), University of Leicester (GB), Queen Mary University of London (GB), King's College London (GB), Barts Health NHS Trust (GB), University of Exeter (GB), St George’s University Hospitals NHS Foundation Trust (GB), University of Oxford (GB), MRC Clinical Trials Unit at UCL (GB), Oxford Medical Diagnostics (United Kingdom) (GB), NIHR Exeter Clinical Research Facility (GB), Institute of Social and Preventive Medicine (CH), William Harvey Research Institute (GB), University College London (GB), University of Sunderland (GB)
Openalex Percentile: Top 2%
Cardiac Imaging and Diagnostics
6.87
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