Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room.

Autor: MacKay EJ; Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA; Penn Center for Perioperative Outcomes Research and Transformation, University of Pennsylvania, Philadelphia, PA; Penn's Cardiovascular Outcomes, Quality and Evaluative Research Center, University of Pennsylvania, Philadelphia, PA; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA. Electronic address: emily.mackay@pennmedicine.upenn.edu., Bharat S; Philips Research, North America, Cambridge, MA., Mukaddim RA; Philips Research, North America, Cambridge, MA., Erkamp R; Philips Research, North America, Cambridge, MA., Sutton J; Philips Research, North America, Cambridge, MA., Muhammad AK; Alabama College of Osteopathic Medicine, Dothan, AL., Savino JS; Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA., Horak J; Department of Anesthesiology and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Jazyk: angličtina
Zdroj: Journal of cardiothoracic and vascular anesthesia [J Cardiothorac Vasc Anesth] 2024 Apr; Vol. 38 (4), pp. 895-904. Date of Electronic Publication: 2024 Jan 11.
DOI: 10.1053/j.jvca.2024.01.005
Abstrakt: Objective: To test the correlation of ejection fraction (EF) estimated by a deep-learning-based, automated algorithm (Auto EF) versus an EF estimated by Simpson's method.
Design: A prospective observational study.
Setting: A single-center study at the Hospital of the University of Pennsylvania.
Participants: Study participants were ≥18 years of age and scheduled to undergo valve, aortic, coronary artery bypass graft, heart, or lung transplant surgery.
Interventions: This noninterventional study involved acquiring apical 4-chamber transthoracic echocardiographic clips using the Philips hand-held ultrasound device, Lumify.
Measurements and Main Results: In the primary analysis of 54 clips, compared to Simpson's method for EF estimation, bias was similar for Auto EF (-10.17%) and the experienced reader-estimated EF (-9.82%), but the correlation was lower for Auto EF (r = 0.56) than the experienced reader-estimated EF (r = 0.80). In the secondary analyses, the correlation between EF estimated by Simpson's method and Auto EF increased when applied to 27 acquisitions classified as adequate (r = 0.86), but decreased when applied to 27 acquisitions classified as inadequate (r = 0.46).
Conclusions: Applied to acquisitions of adequate image quality, Auto EF produced a numerical EF estimate equivalent to Simpson's method. However, when applied to acquisitions of inadequate image quality, discrepancies arose between EF estimated by Auto EF and Simpson's method. Visual EF estimates by experienced readers correlated highly with Simpson's method in both variable and inadequate imaging conditions, emphasizing its enduring clinical utility.
Competing Interests: Declaration of competing interest None.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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