Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study

Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making. One hundred cases were reviewed by 3 highly...

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Published in:	Journal of the Society for Cardiovascular Angiography & Interventions Vol. 3; no. 3; p. 101296
Main Authors:	Rinehart, Sarah, Raible, Steven J., Ng, Nicholas, Mullen, Sarah, Huey, Whitney, Rogers, Campbell, Pursnani, Amit
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01-03-2024
Subjects:	artificial intelligence quantified coronary plaque analysis atherosclerosis coronary artery disease coronary computed tomographic angiography coronary plaque coronary plaque quantification artificial intelligence quantified coronary plaque analysis atherosclerosis coronary artery disease RR CAD IVUS coronary plaque quantification AI-QCPA CACS CCTA coronary plaque coronary computed tomographic angiography LDL TPV
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Summary:	Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making. One hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers. Patients had varying levels of calcium (median CACS: 99.5) and CAD-RADS scores. Initial management plan for each case was a majority decision based upon patient demographics, clinical history, and CCTA report. AI-QCPA was then provided for each patient, and the plan was reconsidered. The primary endpoint was the reclassification rate (RR). In a secondary analysis of 40 cases, the above process was repeated but the initial plan was based upon review of the actual CCTA images. RR following AI-QCPA review was 66% (66/100) of cases (95% CI, 56.72%-75.28%). RR ranged from 47% in cases with CACS 0 to 96% in cases with CACS >400, and from 40% in CAD-RADS 1 cases to 94% in CAD-RADS 4 cases. RR was higher in cases with coronary stenoses ≥50% (89.5%) vs cases with stenoses <50% (51.6%). RR was 39% in cases with LDL <70 mg/dL vs 70% in LDL ≥70 mg/dL. Following review of the CCTA images rather than the CCTA report, the RR was 50% (95% CI of 34.51% - 65.49%). The primary reclassification effect was to intensify preventative medical therapy. Adding AI-QCPA to CCTA alone leads to a change in clinical care in two-thirds of patients.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2772-9303 2772-9303
DOI:	10.1016/j.jscai.2024.101296