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Effect of high-intensity statin therapy on atherosclerosis (IBIS-4): Manual versus automated methods of IVUS analysis.

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Citation: Cardiovascular Revascularization Medicine. 2023 Apr 15PMID: 37087308Institution: MedStar Heart & Vascular InstituteForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXED | Year: 2023 Local holdings: Available in print through MWHC library: 2002 - presentISSN:

1878-0938

Name of journal: Cardiovascular revascularization medicine : including molecular interventionsAbstract: AIMS: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods.CONCLUSIONS: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard. Copyright � 2023. Published by Elsevier Inc.METHODS: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm.RESULTS: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of -0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of -1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of -2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of -3.74 % (p < 0.001).All authors: Bass RD, Blanco PJ, Bourantas CV, Garcia-Garcia HM, Holmvang L, Koskinas KC, Levine MB, Losdat S, Pedrazzini G, Raber L, Roffi M, Shibutani H, Ueki Y, Ziemer PGPFiscal year: FY2023 Digital Object Identifier:

https://dx.doi.org/10.1016/j.carrev.2023.04.007

Date added to catalog: 2023-06-28

Holdings ( 1 )
Title notes ( 6 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	37087308	Available		37087308

Available in print through MWHC library: 2002 - present

AIMS: Standard manual analysis of IVUS to study the impact of anti-atherosclerotic therapies on the coronary vessel wall is done by a core laboratory (CL), the ground truth (GT). Automatic segmentation of IVUS with a machine learning (ML) algorithm has the potential to replace manual readings with an unbiased and reproducible method. The aim is to determine if results from a CL can be replicated with ML methods.

CONCLUSIONS: PAV, the most used endpoint in clinical trials, analyzed by the CL is closely replicated by the ML algorithm. ML automatic segmentation of lumen, vessel and plaque effectively reproduces GT and may be used in future clinical trials as the standard. Copyright � 2023. Published by Elsevier Inc.

METHODS: This is a post-hoc, comparative analysis of the IBIS-4 (Integrated Biomarkers and Imaging Study-4) study (NCT00962416). The GT baseline and 13-month follow-up measurements of lumen and vessel area and percent atheroma volume (PAV) after statin induction were repeated by the ML algorithm.

RESULTS: The primary endpoint was change in PAV. PAV as measured by GT was 43.95 % at baseline and 43.02 % at follow-up with a change of -0.90 % (p = 0.007) while the ML algorithm measured 43.69 % and 42.41 % for baseline and follow-up, respectively, with a change of -1.28 % (p < 0.001). Along the most diseased 10 mm segments, GT-PAV was 52.31 % at baseline and 49.42 % at follow-up, with a change of -2.94 % (p < 0.001). The same segments measured by the ML algorithm resulted in PAV of 51.55 % at baseline and 47.81 % at follow-up with a change of -3.74 % (p < 0.001).

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